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Metaheuristics: A bibliography

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Abstract

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

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References

  • E.H.L. Aarts, E. de Bout, E. Habers and P.J.M. van Laarhoven, Parallel implementations of the statistical cooling algorithm, Integration 4 (1986) 209.

    Google Scholar 

  • E.H.L. Aarts, J. Wessels and P.J. Zwietering, The application of neural networks for decision support, in:Proceedings of the First European Congress on Fuzzy and Intelligent Technologies, ed. H.J. Zimmerman, (Springer, Aachen, 1993) p. 379.

    Google Scholar 

  • E.H.L. Aarts, J.H.M. Korst and P.J. Zwietering, Deterministic and randomized local search, in:Mathematical Perspectives of Neural Networks, ed. P. Smolensky, M. Mozer and D.E. Rumelhart (1995), forthcoming.

  • E.H.L. Aarts, J.H.M. Korst and P.J.M. van Laarhoven, A quantitative analysis of the simulated annealing algorithm. A case study for the travelling salesman problem, Journal of Statistical Physics 50 (1988) 187.

    Article  Google Scholar 

  • E.H.L. Aarts, P.J.M. van Laarhoven, J.K. Lenstra and N.L.J. Ulder, A computational study of local search algorithm for job shop scheduling, ORSA Journal on Computing 6 (1994) 108.

    Google Scholar 

  • E.H.L. Aarts and H.P. Stehouwer, Neural networks and the travelling salesman problem, Working paper, Eindhoven University of Technology, The Netherlands (1993).

    Google Scholar 

  • E.H.L. Aarts and J.H.M. Korst,Simulated Annealing and Boltzmann Machines. A Stochastic Approach to Combinatorial Optimization and Neural Computing (Wiley, Chichester, 1989a).

    Google Scholar 

  • E.H.L. Aarts and J.H.M. Korst, Boltzmann machines for travelling salesman problems, European Journal of Operational Research 39 (1989b) 79.

    Article  Google Scholar 

  • E.H.L. Aarts and J.H.M. Korst, Boltzmann machines as a model for parallel annealing, Algorithmica 6 (1991) 437.

    Article  Google Scholar 

  • E.H.L. Aarts and J.K. Lenstra,Local Search in Combinatorial Optimization (Wiley, Chichester, 1996), forthcoming.

    Google Scholar 

  • E.H.L. Aarts and P.J.M. van Laarhoven, Local search in coding theory, Discrete Mathematics 106 (1992) 11.

    Article  Google Scholar 

  • H. Abada and E. El-Darzi, A metaheuristic for the timetabling problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • W.A.T.W. Abdullah, Seeking global minima, Journal of Computational Physics 110 (1994) 320.

    Article  Google Scholar 

  • S. Abe, J. Kawakami and K. Hirasawa, Solving inequality constrained combinatorial optimization problems by the Hopfield neural networks, Neural Networks 5 (1992) 663.

    Google Scholar 

  • J. Abela, D.A. Abramson, M. Krishnamoorthy, A. Desilva and G. Mills, Computing optimal schedules for landing craft, Working paper, School of Computing and Information Technology, Griffith University, Australia (1993).

    Google Scholar 

  • D.A. Abramson, A very high-speed architecture for simulated annealing, Computer 25 (1992) 27.

    Article  Google Scholar 

  • D.A. Abramson, Constructing school timetables using simulated annealing. Sequential and parallel algorithms, Management Science 37 (1991) 98.

    Google Scholar 

  • D.A. Abramson, G. Mills and S. Perkins, Parallelisation of a genetic algorithm for the computation of efficient train schedules, Working paper, School of Computing and Information Technology, Griffith University, Australia (1993).

    Google Scholar 

  • D.A. Abramson, H. Dang and M. Krishnamoorthy, A comparison of two methods for solving 0–1 integer programs using a general purpose simulated annealing algorithm, Annals of Operations Research 63 (1996) 129.

    Google Scholar 

  • D.A. Abramson and J. Abela, A parallel genetic algorithm for solving the school timetabling problem, Working paper, School of Computing and Information Technology, Griffith University, Australia (1992).

    Google Scholar 

  • F.N. Abuali, D.A. Schoenefeld and R.L. Wainwright, Designing telecommunications networks using genetic algorithms and probabilistic minimum spanning trees, in:Proceedings of the 1994 ACM Symposium on Applied Computing, SAC 1994 (ACM Press, Phoenix 1994) p. 242.

    Google Scholar 

  • F. Abuali, D.A. Schoenefeld and R.L Wainwright, Terminal assignment in a communications network using genetic algorithm, in:Proceedings of the 22nd Annual Computer Science Conference (ACM Press, Phoenix 1994) p. 74.

    Google Scholar 

  • B. Adensodiaz, Restricted neighborhood in the tabu search for the flowshop problem, European Journal of Operational Research 62 (1992) 27.

    Article  Google Scholar 

  • I. Ahmad and M.K. Dhodhi, Task assignment using a problem-space genetic algorithm, Concurrency Practice and Experience 7 (1995a) 411.

    Google Scholar 

  • I. Ahmad and M.K. Dhodhi, On them-way graph partitioning problem, Computer Journal 38 (1995b) 237.

    Google Scholar 

  • R.H. Ahmadi and C.S. Tang, An operation partitioning problem for automated assembly system design, Operations Research 39 (1991) 824.

    Google Scholar 

  • S.V.B. Aiyer, M. Niranjan and F. Fallside, A theoretical investigation into the performance of the Hopfield model, IEEE Transactions on Neural Networks 1 (1990) 204.

    Article  Google Scholar 

  • A.N. Aizawa and B.W. Wah, A sequential sampling procedure for genetic algorithms, Computers and Mathematics with Applications 27 (1994) 77.

    Article  MathSciNet  Google Scholar 

  • A.S. Al-Mahmeed, Tabu search, combination and integration, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • K.S. Al-Sultan, A tabu search approach to the clustering problem, Pattern Recognition 28 (1995) 1443.

    Article  Google Scholar 

  • K.S. Al-Sultan and S.Z. Selim, A global algorithm for the fuzzy clustering problem, Pattern Recognition 26 (1993) 1357.

    Article  Google Scholar 

  • A.S. Alfa, M.Y. Chen and S.S. Heragu, Integrating the grouping and layout problems in cellular manufacturing systems, Computers and Industrial Engineering 23 (1992) 55.

    Article  Google Scholar 

  • A.S. Alfa, S.S. Heragu and M.Y. Chen, A 3-opt based simulated annealing algorithm for vehicle routing problems, Computers and Industrial Engineering 21 (1991) 635.

    Article  Google Scholar 

  • J.R.A. Allwright and D.B. Carpenter, A distributed implementation of simulated annealing for the travelling salesman problem, Parallel Computing 10 (1989) 335.

    Article  Google Scholar 

  • A.E.A. Almaini, J.F. Miller, P. Thomson and S. Billina, State assignment of finite state machines using a genetic algorithm, IEE Proceedings — Computers and Digital Techniques 142 (1995) 279.

    Article  Google Scholar 

  • C.J. Alpert and A.B. Kahng, Recent directions in netlist partitioning. A survey, Integration — The VLSI Journal 19 (1995) 1.

    Google Scholar 

  • I. Althofer and K.U. Koschnick, On the convergence of threshold accepting, Applied Mathematics and Optimization 24 (1991) 183.

    Article  Google Scholar 

  • R. Alvarez-Valdes, G. Martin and J.M. Tamarit, Constructive and tabu search algorithms for the school timetabling problem, Working paper, Department D'Estadistica Investigacio Operativa, Faculdad de Matematicas, Universidad de Valencia, Spain (1993).

    Google Scholar 

  • E. Amaldi, E. Mayoraz and D. de Werra, A review of combinatorial problems arising in feedforward neural network design, Discrete Applied Mathematics 52 (1994) 111.

    Article  Google Scholar 

  • J.K. Amaral, Tumer and J. Ghosh, Designing genetic algorithms for the State Assignment Problem, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 68.

    Google Scholar 

  • S. Amari, A.R. Barron, E. Bienenstock, S. Geman, L. Breiman, J.L. McClelland, B.D. Ripley, R. Tibshirani, B. Cheng and D.M. Titterington, A review from a statistical perspective. Comments and rejoinders, Statistical Science 9 (1994) 31.

    Google Scholar 

  • A. Amberg, W. Domschke and S. Voß, Capacitated minimum spanning trees. Algorithms using intelligent search, Working Paper, Institut für Betriebswirtschaftlehre, Fachgebiet Operations Research, Technische Hochschule Darmstadt, Germany (1995).

    Google Scholar 

  • S. Amellal and B. Kaminska, Functional synthesis of digital-systems with TASS, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13 (1994) 537.

    Article  Google Scholar 

  • M.M. Amini and M. Racer, A rigorous computational comparison of alternative solution methods for the generalized assignment problem, Management Science 40 (1994) 868.

    Google Scholar 

  • C.A. Anderson, K. Fraughnaugh, M. Parker and J. Ryan, Path assignment for call routing: An application of tabu search, Annals of Operations Research 41 (1993) 301.

    Article  Google Scholar 

  • C.A. Anderson, K.F. Jones and J. Ryan, A two-dimensional genetic algorithm for the Ising problem, Complex Systems 5 (1991) 327.

    Google Scholar 

  • E.J. Anderson and M.C. Ferris, Genetic algorithms for combinatorial optimization. The assembly line balancing problem, ORSA Journal on Computing 6 (1994) 161.

    Google Scholar 

  • I.P. Androulakis and V. Venkatasubramanian, A genetic algorithmic framework for process design and optimization, Computers and Chemical Engineering 15 (1991) 217.

    Article  Google Scholar 

  • Y.P. Aneja and M. Parlar, Algorithms for Weber facility location in the presence of forbidden regions and/or barriers to travel, Transportation Science 28 (1994) 70.

    Google Scholar 

  • B. Angeniol, G. de la Crois-Vanbois and J. le Texier, Self-organizing feature maps and the TSP, Neural Networks 1 (1988) 289.

    Article  Google Scholar 

  • B. Angeniol, The neural networks market. A commercial survey, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • S. Anily and A. Federgruen, Simulated annealing methods with general acceptance probabilities, Journal of Applied Probability 24 (1987) 657.

    Google Scholar 

  • N. Ansari, R. Sarasa and G.S. Wang, An efficient annealing algorithm for global optimization in Boltzmann machines, Applied Intelligence 3 (1993) 177.

    Article  Google Scholar 

  • P. Antognetti and V. Milutinovic,Neural Networks. Concepts, Applications, and Implementations (Prentice-Hall, Englewood Cliffs, 1991).

    Google Scholar 

  • G. Anzellotti, R. Battiti, I. Lazzizzera, G. Soncini, A. Sartori, G. Tecchiolli and P. Lee, TOTEM. A highly parallel CHIP for triggering applications with inductive learning based on the reactive tabu search, International Journal of Modern Physics C — Physics and Computers 6 (1995) 555.

    Article  Google Scholar 

  • J. Arabas, A genetic approach to the Hopfield neural-network in the optimization problems, Bulletin of the Polish Academy of Sciences — Chemistry 42 (1994) 59.

    Google Scholar 

  • S. Areibi and A. Vannelli, Circuit partitioning using a tabu search approach, IEEE International Symposium on Circuits and Systems 3 (1993) 1643.

    Google Scholar 

  • I. Arizono, A. Yamamoto and H. Ohta, Scheduling for minimizing total actual flow time by neural networks, International Journal of Production Research 30 (1992) 503.

    Google Scholar 

  • J.S. Arora, M.W. Huang and C.C. Hsieh, Methods for optimization of nonlinear problems with discrete variables. A review, Structural Optimization 8 (1994) 69.

    Article  Google Scholar 

  • S. Arunkumar and T. Chockalingham, Genetic search algorithms and their randomized operators, Computers and Mathematics with Applications 25 (1993) 91.

    Article  Google Scholar 

  • J.B. Atkinson, A greedy look-ahead heuristic for combinatorial optimization. Application to vehicle scheduling with time, Journal of the Operational Research Society 45 (1994) 673.

    Google Scholar 

  • G. Ausiello and M. Protasi, Local search, reducibility and approximability of NP-optimization problems, Information Processing Letters 54 (1995) 73.

    Article  Google Scholar 

  • R. Azencott, Simulated annealing, Asterisque 161 (1988) 223.

    Google Scholar 

  • R. Azencott, Simulated Annealing. Parallelization Techniques (Wiley, Chichester, 1992).

    Google Scholar 

  • G.P. Babu and M.N. Murty, A near optimal initial seed value selection inK-means algorithm using a genetic algorithm, Pattern Recognition Letters 14 (1993) 763.

    Article  Google Scholar 

  • G.P. Babu and M.N. Murty, Simulated annealing for selecting optimal initial seeds in thek-means algorithm, Indian Journal of Pure and Applied Mathematics 25 (1994) 85.

    Google Scholar 

  • F.Q. Bac and V.I. Perov, New evolutionary genetic algorithms for NP-complete combinatorial optimization problems, Biological Cybernetics 69 (1993) 229.

    Article  Google Scholar 

  • T. Bäck, F. Hoffmeister and H.-P. Schwefel, A survey of evolution strategies, in:Proceedings of the 4th International Conference on Genetic Algorithms, ed. R.K. Belew and L.B. Booker (Morgan Kaufmann, San Mateo, 1991) p. 2.

    Google Scholar 

  • T. Bäck, The interaction of mutation rate, selection and self-adaptation within a genetic algorithm, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • T. Bäck and F. Hoffmeister, Basic aspects of evolution strategies, Statistics and Computing 4 (1994) 65.

    Article  Google Scholar 

  • T. Bäck and H.-P. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation 1 (1993) 1.

    Google Scholar 

  • A. Bahrami and C.H. Dagli, Hybrid intelligent packing system (HIPS) through integration of artificial neural networks, artificial-intelligence, and mathematical-programming, Applied Intelligence 4 (1994) 321.

    Article  Google Scholar 

  • F. Baiardi and S. Orlando, Strategies for a massively parallel implementation of simulated annealing, in:Lecture Notes in Computer Science 366 (1989) p. 273.

    Google Scholar 

  • J. Balakrishnan and P.D. Jog, Manufacturing cell-formation using similarity coefficients and a parallel genetic TSP algorithm. Formulation and comparison, Mathematical and Computer Modelling 21 (1995) 61.

    Article  Google Scholar 

  • P.V. Balakrishnan, M.C. Cooper, V.S. Jacob and P.A. Lewis, A study of the classification capabilities of neural networks using unsupervised learning. A comparison withK-means clustering, Psychometrika 59 (1994) 509.

    Google Scholar 

  • P.V. Balakrishnan and V.S. Jacob, Genetic algorithms for product design, Working paper WP93-9-015, College of Business, The Ohio State University (1993).

  • S. Baluja, Structure and performance of fine-grain parallelism in genetic search, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 155.

    Google Scholar 

  • S.H. Bang, O.T.C. Chen, J.C.F. Chang and B.J. Sheu, Paralleled hardware annealing in multilevel Hopfield neural networks for optimal-solutions, IEEE Transactions on Circuits and Systems II — Analog and Digital Signal Processing 42 (1995) 46.

    Article  Google Scholar 

  • W. Banzhaf and F.H. Eckman,Evolution and Biocomputation. Computational Models of Evolution, Lecture Notes in Computer Science 899 (Springer, Berlin, 1995).

    Google Scholar 

  • V.C. Barbosa, A distributed implementation of simulated annealing, Journal of Parallel and Distributed Computing 6 (1989) 411.

    Article  Google Scholar 

  • V.C. Barbosa and M.C.S. Boeres, An OCCAM-based evaluation of a parallel version of simulated annealing, Microprocessing and Microprogramming 30 (1990) 85.

    Article  Google Scholar 

  • J.F. Bard, K. Venkatraman and T.A. Feo, Single machine scheduling with flow time and earliness penalties, Journal of Global Optimization 3 (1993) 289.

    Article  Google Scholar 

  • J.F. Bard and T.A. Feo, An algorithm for the manufacturing equipment selection problem, IIE Transactions 23 (1991) 83.

    Google Scholar 

  • J.F. Bard and T.A. Feo, Operations sequencing in discrete parts manufacturing, Management Science 35 (1989) 249.

    Google Scholar 

  • J.W. Barnes, M. Laguna and F. Glover, An overview of tabu search approaches to production scheduling problems, in:Intelligent Scheduling Systems, ed. D.E. Brown and W.T. Scherer, Operations Research/Computer Science Interfaces 3 (Kluwer, Boston, 1995).

    Google Scholar 

  • J.W. Barnes and J.B. Chambers, Solving the job-shop scheduling problem with tabu search, IIE Transactions 27 (1995) 257.

    Google Scholar 

  • J.W. Barnes and M. Laguna, A tabu search experience in production scheduling, Annals of Operations Research 41 (1993) 141.

    Article  Google Scholar 

  • J.W. Barnes and M. Laguna, Solving the multiple-machine weighted flow time problem using tabu search, IIE Transactions 25 (1993) 121.

    Google Scholar 

  • B.S. Barr, B.L. Golden, J.P. Kelly, M.G.C. Resende and W.R. Stewart, Designing and reporting on computational experiments with heuristic methods, Journal of Heuristics 1 (1995) 9.

    Google Scholar 

  • D. Barrios, J.A.P. Ruydiaz, J. Rios and J. Segovia, Conditions for convergence of genetic algorithms through Walsh-series, Computers and Artificial Intelligence 13 (1994) 441.

    Google Scholar 

  • E.B. Bartlett, A stochastic training algorithm for artificial neural networks, Neurocomputing 6 (1994) 31.

    Article  Google Scholar 

  • R. Battiti, Ractive search. Toward self-tuning heuristics, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • R. Battiti and G. Tecchiolli, The continuous reactive tabu search. Blending combinatorial optimization and stochastic search for global optimization, Annals of Operations Research 63 (1996) 153.

    Google Scholar 

  • R. Battiti, G. Tecchiolli and P. Tonella, Vector quantization with the reactive tabu search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • R. Battiti and G. Tecchiolli, Local search with memory. Benchmarking RTS, OR Spektrum 17 (1995a) 67.

    Article  Google Scholar 

  • R. Battiti and G. Tecchiolli, Training neural nets with the reactive tabu, IEEE Transactions on Neural Networks 6 (1995b) 1185.

    Article  Google Scholar 

  • R. Battiti and G. Tecchiolli, The reactive tabu, ORSA Journal on Computing 6 (1994) 126.

    Google Scholar 

  • R. Battiti and G. Tecchiolli, Parallel biased search for combinatorial optimization. Genetic algorithms and tabu, Microprocessors and Microsystems 16 (1992) 351.

    Article  Google Scholar 

  • D.L. Battle and M.D. Vose, Isomorphisms of genetic algorithms, Artificial Intelligence 60 (1993) 155.

    Article  Google Scholar 

  • R.J. Bauer,Genetic Algorithms and Investment Strategies (Wiley, Chichester, 1994).

    Google Scholar 

  • E.B. Baum, Towards practical neural computation for combinatorial optimization problems, in:Neural Networks for Computing, AIP Conference Proceedings, ed. J.S. Denker (Snowbird, UT, 1986).

    Google Scholar 

  • J.C. Bean, Genetic algorithms and random keys for sequencing and optimization, ORSA Journal on Computing 6 (1994) 154.

    Google Scholar 

  • D. Beasley, D.R. Bull and R.R. Martin, An overview of genetic algorithms 1. Fundamentals, University Computing 15 (1993a) 58.

    Google Scholar 

  • D. Beasley, D.R. Bull and R.R. Martin, An overview of genetic algorithms 2. Research topics, University Computing 15 (1993b) 170.

    Google Scholar 

  • J.E. Beasley, OR-library. Distributed test problems by electronic mail, Journal of the Operational Research Society 41 (1990) 1069 (ftp site address: mscmga.ms.ic.ac.uk).

    Google Scholar 

  • J.E. Beasley and F. Goffinet, A delaunay triangulation-based heuristic for the Euclidean Steiner problem, Networks 24 (1994) 215.

    Google Scholar 

  • J.E. Beasley and P.C. Chu, A genetic algorithm for the set covering problem, Working Paper, The Management School, Imperial College, London (1994).

    Google Scholar 

  • R.K. Belew and L.B. Booker,Proceedings of the Fourth International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1991).

    Google Scholar 

  • C.J.P. Bélisle, Convergence theorems for a class of simulated annealing algorithms on R(D), Journal of Applied Probability 29 (1992) 885.

    Google Scholar 

  • D.A. Bell, F.J. McErlean, P.M. Stewart and S.I. McClean, Application of simulated annealing to clustering tuples in databases, Journal of the American Society for Information Science 41 (1990) 98.

    Article  Google Scholar 

  • H.F. Beltran and D. Skorin-Kapov, On minimum-cost isolated failure immune networks, Telecommunication Systems 3 (1994) 183.

    Article  Google Scholar 

  • M. Benaim and L. Tomasini, Competitive and self-organizing algorithms based on the minimization of an information criterion, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • D. Benarieh and O. Maimon, Annealing method for PCB assembly scheduling on 2 sequential-machines, International Journal of Computer Integrated Manufacturing 5 (1992) 361.

    Google Scholar 

  • M. Bengtsson and Roivainen, Using the Potts-Glass for solving the clustering problem, International Journal of Neural Systems 6 (1995) 119.

    Article  PubMed  Google Scholar 

  • W.A. Bennage and A.K. Dhingra, Single and multi-objective structural optimization in discrete-continuous variables using simulated annealing, International Journal for Numerical Methods in Engineering 38 (1995) 2753.

    Article  Google Scholar 

  • M.S.T. Benten and S.M. Sait, Genetic scheduling of task graphs, International Journal of Electronics 77 (1994) 401.

    Google Scholar 

  • P.J. Bentley, The evolution of solid object designs using genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • A. Bertoni and M. Dorigo, Implicit parallelism in genetic algorithms, Artificial Intelligence 61 (1993) 307.

    Article  Google Scholar 

  • D.J. Bertsimas and J. Tsitsiklis, Simulated annealing, Statistical Science 8 (1993) 10.

    Google Scholar 

  • D.A. Beyer and R.G. Ogier, Tabu learning. A neural network search method for solving nonconvex optimization problems, in:Proceedings of the International Conference on Neural Networks (IEEE Computer Society Press, Los Alamitos, California, 1991) p. 953.

    Google Scholar 

  • D. Bhandari and S.K. Pal, Directed mutation in genetic algorithms, Information Sciences 79 (1994) 251.

    Article  Google Scholar 

  • S. Bhide, N. John and M.R. Kabuka, A Boolean neural network approach for the travelling salesman problem, IEEE Transactions on Computers 42 (1993) 1271.

    Article  Google Scholar 

  • M. Bianchini, M. Gori and M. Maggini, On the problem of local minima in recurrent neural networks, IEEE Transactions on Neural Networks 5 (1994) 167.

    Article  Google Scholar 

  • F. Bicking, C. Fonteix, J.P. Corriou and I. Marc, Global optimization by artificial life. A new technique using genetic population evolution, RAIRO — Operations Research 28 (1994) 23.

    Google Scholar 

  • J.E. Biegel and J.J. Davern, Genetic algorithms and job shop scheduling, Computers and Industrial Engineering 19 (1990) 81.

    Article  Google Scholar 

  • C. Bierwirth, A generalized permutation approach to job shop scheduling with genetic algorithms, OR Spektrum 17 (1995) 87.

    Article  Google Scholar 

  • J. Biethahn and V. Nissen,Evolutionary Algorithms in Management Applications (Springer, Berlin, 1995), forthcoming.

    Google Scholar 

  • G.L. Bilbro and W.E. Snyder, Optimization of functions with many minima, IEEE Transactions on Systems, Man and Cybernetics 21 (1991) 840.

    Google Scholar 

  • G. Bilchev and I.C. Parmee, The ant colony metaphor for searching continuous design spaces,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • A. Bizzarri, Convergence properties of a modified Hopfield—Tank model, Biological Cybernetics 64 (1991) 293.

    Article  PubMed  MathSciNet  Google Scholar 

  • J.A. Bland, A derivative-free exploratory tool for function minimisation based on tabu search, Advances in Engineering Software 19 (1994) 91.

    Article  Google Scholar 

  • J.A. Bland and G.P. Dawson, Large-scale layout of facilities using a heuristic hybrid algorithm, Applied Mathematical Modelling 18 (1994) 500.

    Article  Google Scholar 

  • J.A. Bland and G.P. Dawson, Tabu search and design optimization, Computer-Aided Design 23 (1991) 195.

    Article  Google Scholar 

  • J.J. Bland, Discrete-variable optimal structural design using tabu search, Structural Optimization 10 (1995) 87.

    Article  Google Scholar 

  • J.L. Blanton and R.L. Wainwright, Multiple vehicle routing with time windows using genetic algorithms, in:Proceedings of the Fifth International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 452.

    Google Scholar 

  • J. Blazewicz and R. Walkowiak, A local search approach for 2-dimensional irregular cutting, OR Spektrum 17 (1995) 93.

    Article  Google Scholar 

  • J. Blazewicz, P. Hawryluk and R. Walkowiak, Using a tabu search approach for solving the two-dimensional irregular cutting problem, Annals of Operations Research 41 (1993) 313.

    Article  Google Scholar 

  • F.F. Boctor, A linear formulation of the machine-part cell-formation problem, International Journal of Production Research 29 (1991) 343.

    Google Scholar 

  • K.D. Boese and A.B. Kahng, Best-so-far vs where-you-are. Implications for optimal finite-time annealing, Systems and Control Letters 22 (1994) 71.

    Article  MathSciNet  Google Scholar 

  • N. Boissin and J.-L. Lutton, A parallel simulated annealing algorithm, Parallel Computing 19 (1993) 859.

    Article  MathSciNet  Google Scholar 

  • E. Bonomi and J.-L. Lutton, The asymptotic behaviour of the quadratic sum assignment problem. A statistical mechanics approach, European Journal of Operational Research 26 (1986) 295.

    Article  Google Scholar 

  • E. Bonomi and J.-L. Lutton, TheN-city travelling salesman problem and the metropolis algorithm, SIAM Review 26 (1984) 551.

    Article  Google Scholar 

  • N. Borin, P.W. Farris and J.R. Freeland, A model for determining retail product category assortment and shelf space allocation, Decision Sciences 25 (1994) 359.

    Google Scholar 

  • N. Borin and P. Farris, A sensitivity analysis of retailer shelf management models, Journal of Retailing 71 (1995) 153.

    Article  Google Scholar 

  • J. Bos, Zoning in forest management. A quadratic assignment problem solved by simulated annealing, Journal of Environmental Management 37 (1993) 127.

    Article  Google Scholar 

  • S. Bose and A.R. Saha, Implementation of a heuristic method for standard cell placement, International Journal of Electronics 74 (1993) 281.

    Google Scholar 

  • J. Bovet, C. Constantin and D. de Werra, A convoy scheduling problem, Discrete Applied Mathematics 30 (1991) 1.

    Article  Google Scholar 

  • P. Brandimarte, Neighborhood search-based optimization algorithms for production scheduling. A survey, Computer Integrated Manufacturing Systems 5 (1992) 167.

    Article  Google Scholar 

  • P. Brandimarte, Routing and scheduling in a flexible job shop by tabu search, Annals of Operations Research 41 (1993) 157.

    Article  Google Scholar 

  • P. Brandimarte and M. Calderini, A hierarchical bicriterion approach to integrated process plan selection and job-shop scheduling, International Journal of Production Research 33 (1995) 161.

    Google Scholar 

  • H. Brasel, T. Tautenhahn and F. Werner, Constructive heuristic algorithms for the open shop problem, Computing 51 (1993) 95.

    Google Scholar 

  • H. Braun, On solving travelling salesman problems by genetic algorithms,Lecture Notes in Computer science 496 (1991) 129.

    Google Scholar 

  • C. Brind, C. Muller and P. Prosser, Stochastic techniques for resource management, BT Technology Journal 13 (1995) 55.

    Google Scholar 

  • S.P. Brooks, A hybrid optimization algorithm, Applied Statistics — Journal of the Royal Statistical Society Series C44 (1995) 530.

    Google Scholar 

  • S.P. Brooks and B.J.T. Morgan, Optimization using simulated annealing, Statistician 44 (1995) 241.

    Google Scholar 

  • S.P. Brooks and B.J.T. Morgan, Automatic starting point selection for function optimization, Statistics and Computing 4 (1994) 173.

    Article  Google Scholar 

  • D.E. Brown and C.L. Huntley, A practical application of simulated annealing to clustering, Pattern Recognition 25 (1992) 401.

    Article  Google Scholar 

  • J. Bruck and J. Goodman, On the power of neural networks for solving hard problems, Journal of Complexity 6 (1990) 127.

    Article  Google Scholar 

  • P. Brucker and J. Hurink, Complex sequencing problems and local search heuristics, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • R. Brunelli, Training neural nets through stochastic minimization, Neural Networks 7 (1994) 1405.

    Article  Google Scholar 

  • R. Brunelli, Optimal histogram partitioning using a simulated annealing technique, Pattern Recognition Letters 13 (1992) 581.

    Article  Google Scholar 

  • M.J. Brusco and L.W. Jacobs, Cost-analysis of alternative formulations for personnel scheduling in continuously operating organizations, European Journal of Operational Research 86 (1995) 249.

    Article  Google Scholar 

  • M.J. Brusco and L.W. Jacobs, A simulated annealing approach to the cyclic staff-scheduling problem, Naval Research Logistics 40 (1993a) 69.

    Google Scholar 

  • M.J. Brusco and L.W. Jacobs, A simulated annealing approach to the solution of flexible labor scheduling problems, Journal of the Operational Research Society 44 (1993b) 1191.

    Google Scholar 

  • M.J. Brusco, L.W. Jacobs, R.J. Bongiorno, D.V. Lyons and B.X. Tang, Improving personnel scheduling at airline stations, Operations Research 43 (1995) 741.

    Google Scholar 

  • B.P. Buckles and F.E. Petry,Genetic Algorithms (IEEE Computer Society Press, Los Alamitos, California, 1992).

    Google Scholar 

  • R.S. Bucy and R.S. Diesposti, Decision tree design by simulated annealing, RAIRO — Mathematical Modelling and Numerical Analysis 27 (1993) 515.

    Google Scholar 

  • J. Buhman and H. Kuhnel, Complexity optimized data clustering by competitive neural networks, Neural Computation 5 (1993) 75.

    Google Scholar 

  • T. Bultan and C. Aykanat, Circuit partitioning using mean-field annealing, Neurocomputing 8 (1995) 171.

    Article  Google Scholar 

  • R.E. Burkard and E. Cela, Heuristics for biquadratic assignment problems and their computational comparison, European Journal of Operational Research 83 (1995) 283.

    Article  MathSciNet  Google Scholar 

  • E. Burke, D. Elliman and R. Weare, Specialised recombinative operators for timetabling problems,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • L.I. Burke, A neural design for solution of the maximal independent set problem, European Journal of Operational Research 62 (1992) 186.

    Article  Google Scholar 

  • L.I. Burke and J.P. Ignizio, Neural networks and operations-research. An overview, Computers and Operations Research 19 (1992) 179.

    Article  Google Scholar 

  • A. Burns, N. Hayes and M.F. Richardson, Generating feasible cyclic schedules, Control Engineering Practice 3 (1995) 151.

    Article  Google Scholar 

  • J. Cagan, Shape annealing solution to the constrained geometric knapsack problem, Computer-Aided Design 26 (1994) 763.

    Article  Google Scholar 

  • J. Cagan and W.J. Mitchell, Optimally directed shape generation by shape annealing, Environment and Planning B — Planning and Design 20 (1993) 5.

    Google Scholar 

  • A. Cangelosi, D. Parisi and S. Nolfi, Cell-division and migration in a genotype for neural networks, Network-Computation in Neural Systems 5 (1994) 497.

    Article  Google Scholar 

  • B.Y Cao and G. Uebe, Solving transportation problems with nonlinear side constraints with tabu search, Computers and Operations Research 22 (1995) 593.

    Article  Google Scholar 

  • M.F. Cardoso, R.L. Salcedo and S.F. Deazevedo, Nonequilibrium simulated annealing. A faster approach to combinatorial minimisation, Industrial and Engineering Chemistry Research 33 (1994) 1908.

    Article  Google Scholar 

  • S.E. Carlson, R. Shonkwiler and M.E. Ingrim, Comparison of 3 non-derivative optimization methods with a genetic algorithm for component selection, Journal of Engineering Design 5 (1994) 367.

    Google Scholar 

  • G.A. Carpenter and S. Grossberg, A massively parallel architecture for a self-organizing neural network, Computer Vision, Graphics and Image Processing 37 (1987) 54.

    Article  Google Scholar 

  • H.M. Cartwright and B. Jesson, The analysis of waste flow data from multi-unit industrial complexes using genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • H.M. Cartwright and R.A. Long, Simultaneous optimization of chemical flowshop sequencing and topology using genetic algorithms, Industrial and Engineering Chemistry Research 32 (1993) 2706.

    Article  Google Scholar 

  • A. Casotto, F. Romeo and A. Sangiovanni-Vincentelli, A parallel simulated annealing algorithm for the placement of macro-cells, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 6 (1987) 838.

    Article  Google Scholar 

  • D.J. Castelino, S. Hurley and N.M. Stephens, A tabu search algorithm for frequency assignment, Annals of Operations Research 63 (1996) 301.

    Google Scholar 

  • D.J. Castelino and N.M. Stephens, Tabu thresholding for the frequency assignment problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • O. Catoni, Exponential triangular cooling schedules for simulated annealing algorithms. A case-study,Lecture Notes in Control and Information Sciences 177 (1992a) p. 74.

    Google Scholar 

  • O. Catoni, Rough large deviation estimates for simulated annealing. Application to exponential schedules, Annals of Probability 20 (1992b) 1109.

    Google Scholar 

  • S. Cavalieri, A. Distefano and O. Mirabella, Optimal path determination in a graph by Hopfield neural-networks, Neural Networks 7 (1994) 397.

    Article  Google Scholar 

  • V. Cerny, A thermodynamical approach to the travelling salesman problem. An efficient simulated annealing algorithm, Journal of Optimization Theory and Applications 45 (1985) 41.

    Article  Google Scholar 

  • M. Cesare, J.C. Santamarina, C.J. Turkstra and E. Vanmarcke, Risk-based bridge management. Optimisation and inspection scheduling, Canadian Journal of Civil Engineering 21 (1994) 897.

    Google Scholar 

  • P. Chaisemartin, G. Dreyfus, M. Fontet, E. Kouka, P. Loubieres and P. Siarry, Placement and channel routing by simulated annealing. Some recent developments, Computer Systems Science and Engineering 4 (1989) 35.

    Google Scholar 

  • J. Chakrapani and J. Skorin-Kapov, Connection machine implementation of a tabu search algorithm for the traveling salesman problem, Journal of Computing and Information Technology 1 (1993a) 29.

    Google Scholar 

  • J. Chakrapani and J. Skorin-Kapov, Massively parallel tabu search for the quadratic assignment problem, Annals of Operations Research 41 (1993b) 327.

    Article  Google Scholar 

  • J. Chakrapani and J. Skorin-Kapov, Mapping tasks to processors to minimize communication time in a multiprocessor system, Harriman School for Management Policy, State University of New York, Stony Brook (1993c).

    Google Scholar 

  • J. Chakrapani and J. Skorin-Kapov, A connectionist approach to the quadratic assignment problem, Computers and Operations Research 19 (1992) 287.

    Article  Google Scholar 

  • U.K. Chakravorty and D.G. Dastidar, Using reliability analysis to estimate the number of generations to convergence in genetic algorithms, Information Processing Letters 46 (1993) 199.

    Article  Google Scholar 

  • R.D. Chamberlain, M.N. Edelman, M.A. Franklin and E.E. Witte, Simulated annealing on a multiprocessor, in:Proceedings of the International Conference on Computer Design (1988) p. 540.

  • M. Chams, A. Hertz and D. de Werra, Some experiments with simulated annealing for colouring graphs, European Journal of Operational Research 32 (1987) 260.

    Article  Google Scholar 

  • K.C. Chan and H. Tansri, A study of genetic crossover operations on the facilities layout problem, Computers and Industrial Engineering 26 (1994) 537.

    Article  Google Scholar 

  • W.T. Chan, T.F. Fwa and C.Y. Tan, Road-maintenance planning using genetic algorithms 1. Formulation, Journal of Transportation Engineering — ASCE 120 (1994) 693.

    Google Scholar 

  • R. Chandrasekharam, S. Subramanian and S. Chaudhury, Genetic algorithms for node partitioning problem and applications in VLSI design, IEEE Proceedings — E: Computers and Digital Techniques 140 (1993) 255.

    Google Scholar 

  • R. Chandrasekharam, V.V. Vinod and S. Subramanian, Genetic algorithm for embedding a complete graph in a hypercube with a VLSI application, Microprocessing and Microprogramming 40 (1994a) 537.

    Article  Google Scholar 

  • R. Chandrasekharam, V.V. Vinod and S. Subramanian, Genetic algorithm for test scheduling with different objectives, Integration — The VLSI Journal 17 (1994b) 153.

    Article  Google Scholar 

  • P.C. Chang and R.C. Hsu, A simulated annealing approach to parallel processor scheduling problems with precedence relations, Journal of the Chinese Institute of Engineers 17 (1994) 485.

    Google Scholar 

  • R.I. Chang and P.Y. Hsiao, Solving system partitioning problem using a massively parallel bio-computing network, IFIP Transactions A — Computer Science and Technology 51 (1994) 129.

    Google Scholar 

  • T.M. Chang and Y. Yih, Determining the number of kanbans and lotsizes in a generic kanban system. A simulated annealing approach, International Journal of Production Research 32 (1994) 1991.

    Google Scholar 

  • C.D. Chapman, K. Saitou and M.J. Jakiela, Genetic algorithms as an approach to configuration and topology design, Journal of Mechanical Design 116 (1994) 1005.

    Google Scholar 

  • P. Chardaire, Location of concentrators using simulated annealing, in:Applications of Modern Heuristics Methods, ed. V.J. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995).

    Google Scholar 

  • P. Chardaire, J.-L. Lutton and A. Sutter, Thermostatistical persistency. A powerful improving concept for simulated annealing algorithms, European Journal of Operational Research 86 (1995) 565.

    Article  Google Scholar 

  • P. Chardaire and J.-L. Lutton, Using simulated annealing to solve concentrator location problems in telecommunication networks,Lecture Notes in Economics and Mathematical Systems 396 (1993) p. 176.

    Google Scholar 

  • I. Charon and O. Hudry, Mixing different components of metaheuristics, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • I. Charon and O. Hudry, The noising method. A new method for combinatorial optimization, Operations Research Letters 14 (1993) 133.

    Article  MathSciNet  Google Scholar 

  • K.M. Cheh, Goldberg J.B. and R.G. Askin, A note on the effect of neighborhood-structure in simulated annealing, Computers and Operations Research 18 (1991) 537.

    Article  Google Scholar 

  • C. Chen, F. Swift and R. Racine, A computer application in apparel manufacturing management, Computers and Industrial Engineering 23 (1992) 439.

    Article  Google Scholar 

  • C.L. Chen, N.A. Cotruvo and W. Baek, A simulated annealing solution to the cell-formation problem, International Journal of Production Research 33 (1995) 2601.

    Google Scholar 

  • C.L. Chen, V.S. Vempati and N. Aljaber, An application of genetic algorithms for flow-shop problems, European Journal of Operational Research 80 (1995) 389.

    Article  Google Scholar 

  • H.C. Chen, Machine learning for information-retrieval. Neural networks, symbolic learning and genetic algorithms, Journal of the American Society for Information Science 46 (1995) 194.

    Article  Google Scholar 

  • L.N. Chen and K. Aihara, Chaotic simulated annealing by a neural-network model with transient chaos, Neural Networks 8 (1995) 915.

    Article  Google Scholar 

  • S.K. Chen, P. Mangiameli and D. West, The comparative ability of self-organizing neural networks to define cluster structure, Omega 23 (1995) 271.

    Article  Google Scholar 

  • W.H. Chen and B. Srivastava, Simulated annealing procedures for forming machine cells in group technology, European Journal of Operational Research 75 (1994) 100.

    Article  Google Scholar 

  • Y.L. Chen and C.C. Liu, Optimal multiobjective var planning using an interactive satisfying method, IEEE Transactions on Power Systems 10 (1995) 664.

    Article  Google Scholar 

  • B. Cheng and D.M. Titterington, Neural networks. A review from a statistical perspective, Statistical Science 9 (1994) 2.

    Google Scholar 

  • R.W. Cheng, M. Gen and M. Sasaki, Film-copy deliverer problem using genetic algorithms, Computers and Industrial Engineering 29 (1995) 549.

    Article  Google Scholar 

  • R.W. Cheng and M. Gen, Crossover on intensive search and traveling salesman problem, Computers and Industrial Engineering 27 (1994) 485.

    Article  Google Scholar 

  • M. Chester,Neural Networks. A Tutorial (Prentice-Hall, Englewood Cliffs, 1993).

    Google Scholar 

  • W.-C. Chiang and R.A. Russell, Simulated annealing metaheuristics for the vehicle routing problem with time windows, Annals of Operations Research 63 (1996) 3.

    Google Scholar 

  • W.-C. Chiang and P. Kouvelis, Simulated annealing and tabu search approaches to undirectional flowpath design for automated guided vehicle systems, Annals of Operations Research 50 (1994) 115.

    Article  Google Scholar 

  • C. Chiu, C.Y. Maa and M.A. Shanblaff, An artificial neural network algorithm for dynamic programming, International Journal of Neural Systems 1 (1990) 211.

    Article  Google Scholar 

  • T. Chockalingam and S. Arunkumar, Genetic algorithm-based heuristics for the mapping problem, Computers and Operations Research 22 (1995) 55.

    Article  Google Scholar 

  • T. Chockalingam and S. Arunkumar, A randomized heuristic for the mapping problem. The genetic approach, Parallel Computing 18 (1992) 1157.

    Article  Google Scholar 

  • C.J. Chou, C.C. Liu and Y.T. Hsiao, A multiobjective optimization approach to loading balance and grounding planning in 3-phase 4-wire distribution-systems, Electric Power Systems Research 31 (1994) 163.

    Article  Google Scholar 

  • M. Christoph and K.H. Hoffmann, Scaling behavior of optimal simulated annealing schedules, Journal of Physics A — Mathematical and General 26 (1993) 3267.

    Article  Google Scholar 

  • C.H. Chu and D. Widjaja, Neural network system for forecasting method selection, Decision Support Systems 12 (1994) 13.

    Article  Google Scholar 

  • P.C. Chu and J.E. Beasley, A genetic algorithm for the generalized assignment problem, Working paper, The Management School, Imperial College, London (1995).

    Google Scholar 

  • Y.K. Chung and G.W. Fischer, A neural algorithm for finding the shortest flow path for an automated guided vehicle system, IIE Transactions 27 (1995) 773.

    Google Scholar 

  • CHARME, Bull. CEDIAG, Louveciennes, France (1993).

  • CHIP, Reference Manual, COSYTEC, Parc Club Orsay-Université, Orsay, France (1993).

    Google Scholar 

  • A. Cichocki and R. Unbehauen,Neural Networks for Optimization and Signal Processing (Wiley, New York, 1993).

    Google Scholar 

  • S.E. Cieniawski, J.W. Eheart and S. Ranjithan, Using genetic algorithms to solve a multi-objective groundwater monitoring problem, Water Resources Research 31 (1995) 399.

    Article  Google Scholar 

  • G.A. Cleveland and S.F. Smith, Using genetic algorithms to schedule flow shop release, in:Proceedings of the 3rd International Conference on Genetic Algorithms, ed. J.D. Schaffer (Morgan Kaufmann, San Mateo, 1989) p. 160.

    Google Scholar 

  • J.P. Cohoon, S.U. Hegde, W.N. Martin and D.S. Richards, Distributed genetic algorithms for the floorplan design problem, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 10 (1991) 483.

    Article  Google Scholar 

  • J.P. Cohoon, W.N. Martin and D. Richards, A multi-population genetic algorithm for solving theK-partition problem on hyper-cubes, in:Proceedings of the 4th International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo 1991) p. 244.

    Google Scholar 

  • N.E. Collins, R.W. Eglese and B.L. Golden, Simulated annealing. An annotated bibliography, American Journal of Mathematical and Management Sciences 9 (1988) 209.

    Google Scholar 

  • A. Colorni, M. Dorigo, V. Maniezzo and M. Trubian, Ant systems for job shop scheduling, Belgian Journal of Operations Research, Statistics and Computer Science 34 (1994) 39.

    Google Scholar 

  • A. Colorni, M. Dorigo and V. Maniezzo, An investigation of some properties of an ant algorithm, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam 1992a).

    Google Scholar 

  • A. Colorni, M. Dorigo and V. Maniezzo, Genetic algorithms: A new approach to the timetable problem, in:Combinatorial Optimization, ed. M. Akgul, W. Horst, W. Hamcher and S. Tuefekci (Springer, Berlin 1992b).

    Google Scholar 

  • A. Colorni, M. Dorigo and V. Maniezzo, Genetic algorithms and highly constrained problems. The time-table case,Lecture Notes in Computer Science 496 (1991a) p. 55.

    Google Scholar 

  • A. Colorni, M. Dorigo and V. Maniezzo, Positive feedback as a search strategy, Working paper 91-16, Department of Electronics, Politecnico di Milano, Italy (1991b).

    Google Scholar 

  • M. Conlon, The controlled random search procedure for function optimization, Communications in Statistics-Simulation and Computation 21 (1992) 919.

    Google Scholar 

  • D.T. Connolly, General purpose simulated annealing, Journal of the Operational Research Society 43 (1992) 495.

    Google Scholar 

  • D.T. Connolly, An improved annealing schedule for the QAP, European Journal of Operational Research 46 (1990) 93.

    Article  Google Scholar 

  • D.G. Conway and M.A. Venkataramanan, Genetic search and the dynamic facility layout problem, Computers and Operations Research 21 (1994) 955.

    Article  Google Scholar 

  • J.S. Cook and B.T. Han, Efficient heuristics for robot acquisition planning for a CIM system, OR Spektrum 17 (1995) 99.

    Article  Google Scholar 

  • A.L. Corcoran and R.L. Wainwright, A genetic algorithm for packing in three dimensions, in:Proceedings of the 1992 ACM/SIGAPP Symposium on Applied Computing (ACM Press, Kansas, 1992) p. 1021.

    Google Scholar 

  • J.-F. Cordeau, M. Gendreau and G. Laporte, A tabu search heuristic for periodic and multidepot vehicle routing problems, Working paper CRT-95-75, Centre de recherche sur les transports, Montreal (1995).

  • D. Corne and P. Ross, Some combinatorial landscapes on which a genetic algorithm outperforms other stochastic iterative methods,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • D. Costa, An evolutionary tabu search algorithm and the NHL scheduling problem, INFOR 33 (1995) 161.

    Google Scholar 

  • D. Costa, A tabu search algorithm for computing an operational timetable, European Journal of Operational Research 76 (1994) 98.

    Article  Google Scholar 

  • D. Costa, A. Hertz and O. Dubuis, Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs, Journal of Heuristics 1 (1995) 105.

    Google Scholar 

  • P. Courrieu, A convergent generator of neural networks, Networks 6 (1993) 835.

    Google Scholar 

  • I.B. Crabtree, Resource scheduling. Comparing simulated annealing with constraint programming, BT Technology Journal 13 (1995) 121.

    Google Scholar 

  • G. Craig, M. Krishnamoorthy and M. Palaniswami, Comparison of heuristic algorithms for the degree constrained minimum spanning tree, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • T.G. Crainic, M. Gendreau, P. Soriano and M. Toulouse, A tabu search procedure for multi-commodity location-allocation with balancing requirements, Annals of Operations Research 41 (1993) 359.

    Article  Google Scholar 

  • T.G. Crainic, M. Toulouse and M. Gendreau, Parallel asynchronous tabu search for multi-commodity location-allocation with balancing requirements, Annals of Operations Research 63 (1996) 277.

    Google Scholar 

  • T.G. Crainic, M. Toulouse and M. Gendreau, Synchronous tabu search parallelization strategies for multicomodity location-allocation with balancing requirements, OR Spektrum 17 (1995) 113.

    Article  Google Scholar 

  • H. Crockett, Applications of neural networks in finance,Proceedings of the ASIS Annual Meeting 31 (1994) p. 105.

    Google Scholar 

  • A.E. Croker and V. Dhar, A knowledge representation for constraint satisfaction problems, IEEE Transactions on Knowledge and Data Engineering 5 (1993) 740.

    Article  Google Scholar 

  • D. Cubanski and D. Cyganski, Multivariate classification through adaptive Delaunay-based C-0 spline approximation, IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 403.

    Article  Google Scholar 

  • M. Cuppini, A genetic algorithm for channel assignment problems, European Transactions on Telecommunications and Related Technologies 5 (1994) 285.

    Google Scholar 

  • F. Curatelli, Implementation and evaluation of genetic algorithms for system partitioning, International Journal of Electronics 78 (1995) 435.

    Google Scholar 

  • S.P. Curram and J. Mingers, Neural networks, decision tree induction and discriminant analysis. An empirical comparison, Journal of the Operational Research Society 45 (1994) 440.

    Google Scholar 

  • A.J. Cuticchia, J. Arnold and W.E. Timberlake, The use of simulated annealing in chromosome reconstruction experiments based on binary scoring, Genetics 132 (1992) 591.

    PubMed  Google Scholar 

  • R. Cuykendall and R. Reese, Scaling the neural TSP algorithm, Biological Cybernetics 60 (1989) 365.

    Article  Google Scholar 

  • C.H. Dagli and S. Sittisathanchai, Genetic neuro-scheduler for job shop scheduling, Computers and Industrial Engineering 25 (1993) 267.

    Article  Google Scholar 

  • B. Dahlin and O. Sallnas, Harvest scheduling under adjacency constraints. A case study from the Swedish sub-alpine region, Scandinavian Journal of Forest Research 8 (1993) 281.

    Google Scholar 

  • F. Dammeyer, P. Forst and S. Voß, On the cancellation sequence method of tabu search, ORSA Journal on Computing 3 (1991) 262.

    Google Scholar 

  • R.L. Daniels and J.B. Mazzola, A tabu search heuristic for the flexible resource flow shop scheduling problem, Annals of Operations Research 41 (1993) 207.

    Article  Google Scholar 

  • A. Danjou, M. Grana, F.J. Torrealdea and M.C. Hernandez, Solving satisfiability via Boltzmann machines, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993) 514.

    Article  Google Scholar 

  • F. Darema, S. Kirkpatrick and V.A. Norton, Parallel algorithms for chip placement by simulated annealing, IBM Journal of Research and Development 31 (1987) 391.

    Google Scholar 

  • H. Das, P.T. Cummings and M.D. Levan, Scheduling of serial multiproduct batch processes via simulated annealing, Computers and Chemical Engineering 14 (1990) 1351.

    Article  Google Scholar 

  • Y. Davidor, H.-P. Schwefel and R. Männer,Parallel Problem Solving from Nature. PPSN III Proceedings, Lecture Notes in Computer Science 866 (Springer, Berlin, 1994).

    Google Scholar 

  • L. Davis,Handbook of Genetic Algorithms (Van Nostrand Reinhold, New York, 1991).

    Google Scholar 

  • L. Davis,Genetic Algorithms and Simulated Annealing (Pitman, London, 1987).

    Google Scholar 

  • L. Davis and A. Cox, A genetic algorithm for survivable network design, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 408.

    Google Scholar 

  • M. de la Maza and D. Yuret, Dynamic hill climbing, AI Expert 9 (1994) 26.

    Google Scholar 

  • D. de Werra and A. Hertz, Tabu search techniques. A tutorial and an application to neural networks, OR Spektrum 11 (1989) 131.

    Article  Google Scholar 

  • S.G. Deamorim, J.P. Barthelemy and C.C. Ribeiro, Clustering and clique partitioning. Simulated annealing and tabu search approaches, Journal of Classification 9 (1992) 17.

    MathSciNet  Google Scholar 

  • G.J. Deboeck,The Trading Edge. Neural, Genetic and Fuzzy Systems for Chaotic and Financial Markets (Wiley, Chichester, 1994).

    Google Scholar 

  • R. Dechter, Constraint networks, in:Encyclopedia of Artifical Intelligence, Vol. 1, ed. S.C. Shaprio (Wiley, Chichester, 1992).

    Google Scholar 

  • R. Dechter, Enhancement schemes for constraint satisfaction processing. Backjumping, learning and cutset decomposition, Artificial Intelligence 41 (1990) 273.

    Article  Google Scholar 

  • R. Dechter and I. Meiri, Experimental evaluation of preprocessing algorithms for constraint satisfaction problems, Artificial Intelligence 68 (1994) 211.

    Article  Google Scholar 

  • R. Dechter and J. Pearl, Network based heuristics for the constraint satisfaction problems, Artifical Intelligence 34 (1988) 1.

    Article  Google Scholar 

  • A. Degloria, P. Faraboschi and M. Olivieri, Block placement with a Boltzmann machine, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13 (1994) 694.

    Article  Google Scholar 

  • A. Degloria, P. Faraboschi and M. Olivieri, Clustered Boltzmann machines. Massively parallel architectures for constrained optimization problems, Parallel Computing 19 (1993a) 163.

    Article  Google Scholar 

  • A. Degloria, P. Faraboschi and M. Olivieri, Design of a massively parallel SIMD architecture for the Boltzmann machine, Microprocessing and Microprogramming 37 (1993b) 153.

    Article  Google Scholar 

  • C. Degroot, D. Wurtz and K.H. Hoffmann, Optimizing complex problems by nature algorithms. Simulated annealing and evolution strategy. A comparative study,Lecture Notes in Computer Science 496 (1991) p. 445.

    Google Scholar 

  • C. Degroot, D. Wurtz and K.H Hoffmann, Simulated annealing and evolution strategy. A comparison, Helvetica Physica Acta 63 (1990) 843.

    Google Scholar 

  • K.A. DeJong and W.M. Spears, On the state of evolutionary computation, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 618.

    Google Scholar 

  • K.A. DeJong and W.M. Spears, An analysis of the interacting roles of population-size and crossover in genetic algorithms,Lecture Notes in Computer Science 496 (1991) p. 38.

    Google Scholar 

  • K.A. DeJong and W.M. Spears, Using genetic algorithms to solve NP-complete problems, in:Proceedings of the 3rd International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1989) p. 124.

    Google Scholar 

  • K.A. DeJong, W.M. Spears and D.A. Gordon, Using genetic algorithms for concept learning, in:Genetic Algorithms for Machine Learning, ed. J.J. Grefenstette (Kluwer, Boston, 1994).

    Google Scholar 

  • A. Dekkers and E.H.L. Aarts, Global optimization and simulated annealing, Mathematical Programming 50 (1991) 367.

    Article  Google Scholar 

  • M. Dell'Amico and F. Maffioli, A new tabu search approach to the 0–1 equicut problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • M. Dell'Amico, S. Martello and D. Vigo, Heuristic algorithms for single processor scheduling with earliness and flow time penalties, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • M. Dell'Amico and M. Trubian, Applying tabu search to the job-shop scheduling problem, Annals of Operations Research 41 (1993) 231.

    Article  Google Scholar 

  • F.D. DellaCroce, R. Tadei, and G. Volta, A genetic algorithm for the job shop problem, Computers and Operations Research 22 (1995) 15.

    Article  Google Scholar 

  • F.D. DellaCroce, R. Tadei and R. Rolando, Solving a real-world project scheduling problem with a genetic approach, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 65.

    Google Scholar 

  • P.J. Denning, Genetic algorithms, American Scientist 80 (1992) 12.

    Google Scholar 

  • W.S. Desarbo, R.L. Oliver and A. Rangaswamy, A simulated annealing methodology for clusterwise linear-regression, Psychometrika 54 (1989) 707.

    Google Scholar 

  • A.K. Dhingra and W.A. Bennage, Discrete and continuous variable structural optimization using tabu search, Engineering Optimization 24 (1995) 177.

    Google Scholar 

  • M.K. Dhodhi, F.H. Hielscher, R.H. Storer and J. Bhasker, Datapath synthesis using a problem-space genetic algorithm, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 14 (1995) 934.

    Article  Google Scholar 

  • R. Diekmann, R. Luling and J. Simon, Problem independent distributed simulated annealing and its applications,Lecture Notes in Economics and Mathematical Systems 396 (1993) p. 17.

    Google Scholar 

  • P. Dige, C. Lund and H.F. Ravn, Timetabling by simulated annealing,Lecture Notes in Economics and Mathematical Systems 396 (1993) p. 1151.

    Google Scholar 

  • H. Ding, A.A. Elkeib and R. Smith, Optimal clustering of power networks using genetic algorithms, Electric Power Systems Research 30 (1994) 209.

    Article  Google Scholar 

  • N. Dodd, Slow annealing versus multiple fast annealing runs. An empirical investigation, Parallel Computing 16 (1990) 269.

    Article  Google Scholar 

  • W.B. Dolan, P.T. Cummings and M.D. Levan, Algorithmic efficiency of simulated annealing for heat-exchanger network design, Computers and Chemical Engineering 14 (1990) 1039.

    Article  Google Scholar 

  • W. Domschke, Schedule synchronization for public transit networks, OR Spektrum 11 (1989) 17.

    Article  Google Scholar 

  • W. Domschke, P. Forst and S. Voß, Tabu search techniques for the quadratic semi-assignment problem, in:New Directions for Operations Research in Manufacturing, ed. G. Fandel, T. Gulledge and A. Jones (Springer, Berlin, 1992).

    Google Scholar 

  • W. Domschke and G. Krispin, Location and layout planning. A survey, Working Paper, Institut für Betriebswirtschaftlehre, Fachgebiet Operations Research, Technische Hochschule Darmstadt, Germany (1996).

    Google Scholar 

  • M. Dorigo, Using transputers to increase speed and flexibility of genetics based machine learning systems, Microprocessing and Microprogamming 34 (1992) 147.

    Article  Google Scholar 

  • M. Dorigo and V. Maniezzo, Parallel genetic algorithms. Introduction and overview of current research, in:Parallel Genetic Algorithms. Theory and Applications, ed. J. Stender (ISO Press, Amsterdam, 1992).

    Google Scholar 

  • M. Dorigo, V. Maniezzo and A. Colorni, The ant system. Optimizaton by a colony of cooperating agents, IEEE Transaction on Systems, Man and Cybernetics (1995), forthcoming.

  • J. Dorn, R. Kerr and G. Thalhammer, Reactive scheduling. Improving the robustness of schedules and restricting the effects of shop-floor disturbances by fuzzy-reasoning, International Journal of Human-Computer Studies 42 (1995) 287.

    Article  Google Scholar 

  • U. Dorndorf and E. Pesch, Evolution-based learning in a job shop scheduling environment, Computers and Operations Research 22 (1995) 25.

    Article  Google Scholar 

  • U. Dorndorf and E. Pesch, Fast clustering algorithms, ORSA Journal on Computing 6 (1994) 141.

    Google Scholar 

  • R. Dorne and J.K. Hao, An evolutionary approach for frequency assignment in cellular radio networks, in:Proceedings of the of IEEE International Conference on Evolutionary Computation, ICEC'95 (IEEE Computer Society Press, Los Alamitos, California, 1995).

    Google Scholar 

  • R.E. Dorsey and W.J. Mayer, Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features, Journal of Business and Economic Statistics 13 (1995) 53.

    Google Scholar 

  • K.A. Dowsland, Simple tabu thresholding and the pallet loading problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • K.A. Dowsland, Simulated annealing solutions for multi-objective scheduling and timetabling, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • K.A. Dowsland, Some experiments with simulated annealing techniques for packing problems, European Journal of Operational Research 68 (1993) 389.

    Article  Google Scholar 

  • K.A. Dowsland, Hillclimbing, simulated annealing and the Steiner problem in graphs, Engineering Optimization 17 (1991) 91.

    Google Scholar 

  • K.A. Dowsland, A timetabling problem in which clashes are inevitable, Journal of the Operational Research Society 41 (1990) 907.

    Google Scholar 

  • A. Drexl, A simulated annealing approach to the multiconstraint zero-one knapsack problem, Computing 40 (1988) 1.

    Google Scholar 

  • A. Drexl and K. Haase, Sequential analysis based randomized regret methods for lot sizing and scheduling, Journal of Operational Research Society 47 (1996) 251.

    Google Scholar 

  • A. Drexl, J. Juretzka and F. Salewski, Academic course scheduling under workload and changeover constraints, Working paper No. 337, Institut für Betriebswirtschaftslehre der Universität Kiel, Germany (1993).

    Google Scholar 

  • N. Dubois and D. de Werra, EPCOT. An efficient procedure for colouring optimally with tabu search, Computers and Mathematics with Applications 25 (1993) 35.

    Article  MathSciNet  Google Scholar 

  • E.J. Dubuc, Bandwidth reduction by simulated annealing, International Journal for Numerical Methods in Engineering 37 (1994) 3977.

    Article  Google Scholar 

  • G. Dueck and T. Scheuer, Threshold accepting. A general purpose optimization algorithm appearing superior to simulated annealing, Journal of Computational Physics 90 (1990) 161.

    Article  MathSciNet  Google Scholar 

  • H. Dufourd, M. Gendreau and G. Laporte, Locating a transit line using tabu search, Location Science (1996), forthcoming.

  • T. Duncan, A review of commercially available constraint programming tools, Working paper AIAI-TR-149, Artificial Intelligence Applications Institute, University of Edinburgh (1994a).

  • T. Duncan, Intelligent vehicle scheduling. Experiences with a constraint based approach, Working paper AIAI-TR-150, Artificial Intelligence Applications Institute, University of Edinburgh (1994b).

  • M. Duqueanton, D. Kunz and B. Ruber, Channel assignment for cellular radio using simulated annealing, IEEE Transactions on Vehiclular Technology 42 (1993) 14.

    Article  Google Scholar 

  • M.D. Durand, Parallel simulated annealing. Accuracy vs speed in placement, IEEE Design and Test of Computers 6 (1989) 8.

    Google Scholar 

  • R. Durbin, R. Szeliski and A. Yuille, An analysis of the elastic net approach to the TSP, Neural Computation 2 (1990) 348.

    Google Scholar 

  • R. Durbin, R. Szeliski and A. Yuille, An analysis of the elastic net approach to the travelling salesman problem, Neural Computation 1 (1989) 348.

    Google Scholar 

  • R. Durbin and D. Willshaw, An analogue approach to the TSP using an elastic net method, Nature 326 (1987) 689.

    Article  PubMed  Google Scholar 

  • F.F. Easton and N. Mansour, A distributed genetic algorithm for the employee staffing and scheduling problem, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 360.

    Google Scholar 

  • M. Efe, Statistical-analysis of parallel randomized algorithms for VLSI placement and implementation on workstation networks, Microprocessors and Microsystems 19 (1995) 341.

    Article  Google Scholar 

  • R.W. Eglese, Routing winter gritting vehicles, Discrete Applied Mathematics 48 (1994) 231.

    Article  Google Scholar 

  • R.W. Eglese, Simulated annealing. A tool for operational research, European Journal of Operational Research 46 (1990) 271.

    Article  Google Scholar 

  • R.W. Eglese, Heuristics in operational research, in:Recent Developments in Operational Research, ed. V. Belton and B. O'Keefe (Pergamon Press, Oxford, 1986).

    Google Scholar 

  • R.W. Eglese and L.Y.O. Li, A tabu search based heuristic for arc routing with a capacity constraint and time deadline, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • A.E. Eiben, E.H.L. Aarts and K.M. van Hee, Global convergence of genetic algorithms. A Markov-chain analysis,Lecture Notes in Computer Science 496 (1991) p. 4.

    Google Scholar 

  • S.S. Erenguc and H. Pirkul, Heuristic, genetic and tabu search. Foreword, Computers and Operations Research 21 (1994) 799.

    Article  Google Scholar 

  • E. Erwin, K. Obermayer and K. Schulten, Convergence properties of self-organizing maps, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • R. Ettelaie and M.A. Moore, Zero-temperature scaling and simulated annealing, Journal de Physique 48 (1987) 1255.

    Google Scholar 

  • U. Faigle and W. Kern, On the convergence of simulated annealing algorithms, SIAM Journal on Control and Optimization 29 (1991) 153.

    Article  Google Scholar 

  • U. Faigle and B. Schrader, On the convergence of stationary distributions in simulated annealing algorithms, Information Processing Letters 27 (1988) 189.

    Article  Google Scholar 

  • U. Faigle and W. Kern, Some convergence results for probabilistic tabu search, ORSA Journal on Computing 4 (1992) 32.

    Google Scholar 

  • A. Fairley and D.F. Yates, An alternative method of choosing the crossover point when solving the knapsack problem with genetic algorithms, Working paper, Department of Computer Science, University of Liverpool (1993).

  • E. Falkenauer, Solving equal piles with a grouping genetic algorithm, in:Proceedings of the 6th International Conference on Genetic Algorithms, ICGA95 (Morgan Kaufmann, San Mateo, 1995) p. 492.

    Google Scholar 

  • E. Falkenauer, A new representation and operators for GAs applied to grouping problems, Evolutionary Computation 2 (1994) 123.

    Google Scholar 

  • E. Falkenauer, The grouping genetic algorithms. Widening the scope of the GAs, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 79.

    Google Scholar 

  • E. Falkenauer, A genetic algorithm for grouping, in:Proceedings of the 5th International Symposium on Applied Stochastic Models and Data Analysis (World Scientific Press, Singapore, 1991) p. 198.

    Google Scholar 

  • E. Falkenauer and S. Bouffouix, A genetic algorithm for job shop, in:Proceedings of the 1991 IEEE International Conference on Robotics and Automation (IEEE Computer Society Press, Los Alamitos, California, 1991) p. 824.

    Google Scholar 

  • E. Falkenauer and A. Delchambre, A genetic algorithm for bin packing and line balancing, in:Proceedings of the 1992 IEEE International Conference on Robotics and Automation (IEEE Computer Society Press, Los Alamitos, California, 1992) p. 1186.

    Google Scholar 

  • E. Falkenauer and P. Gaspart, Creating part families with a grouping genetic algorithm, in:Proceedings of the International Symposium on Intelligent Robotics, ISIR '93, ed. M. Vidyasagar (McGraw-Hill, New Delhi, 1993) p. 375.

    Google Scholar 

  • H.L. Fang, P. Ross and D. Corne, A promising genetic algorithm approach to job-shop scheduling, rescheduling and open-shop scheduling problems, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 375.

    Google Scholar 

  • L.V. Fausett, Fundamentals of Neural Networks. Architectures, Algorithms, and Applications (Prentice-Hall, Englewood Cliffs, 1994).

    Google Scholar 

  • F. Favata and R. Walker, A study of the application of Kohonen type neural networks to the traveling salesman problem, Biological Cybernetics 64 (1991) 463.

    Article  Google Scholar 

  • T.A. Feo, A GRASP for scheduling printed wiring based board assembly, Working paper, Operations Research Group, Department of Mechanical Engineering, The University of Texas at Austin (1993).

  • T.A. Feo, The cutting path and tool selection problem in computer-aided process planning, Journal of Manufacturing Systems 8 (1989) 17.

    Google Scholar 

  • T.A. Feo and J.F. Bard, Flight scheduling and maintenance base planning, Management Science 35 (1989) 1415.

    Google Scholar 

  • T.A. Feo and M.G.C. Resende, Greedy randomized adaptive search procedures, Journal of Global Optimization 6 (1995) 109.

    Article  MathSciNet  Google Scholar 

  • T.A. Feo, M.G.C. Resende and S.H. Smith, A greedy randomized adaptive search procedure for maximum independent set, Operations Research 42 (1994) 860.

    Google Scholar 

  • T.A. Feo and M.G.C. Resende, A probabilistic heuristic for a computationally difficult set covering problem, Operations Research Letters 8 (1989) 67.

    Article  Google Scholar 

  • T.A. Feo, K. Sarathy and J. McGahan, A GRASP for single machine scheduling with sequence dependent set, Working paper, Operations Research Group, Department of Mechanical Engineering, The University of Texas at Austin (1994).

  • T.A. Feo, K. Venkatraman and J.F. Bard, A GRASP for a difficult single machine scheduling problem, Computers and Operations Research 18 (1991) 635.

    Article  Google Scholar 

  • M. Ferri and M. Piccioni, Optimal selection of statistical units. An approach via simulated annealing, Computational Statistics and Data Analysis 13 (1992) 47.

    Article  MathSciNet  Google Scholar 

  • C.N. Fiechter, A parallel tabu search algorithm for large traveling salesman problems, Discrete Applied Mathematics 51 (1994) 243.

    Article  Google Scholar 

  • B. Filipic, Enhancing genetic search to schedule a production unit, in:Proceedings of the 10th European Conference on Artificial Intelligence, ed. B. Neumann (Wiley, New York, 1992) p. 603.

    Google Scholar 

  • A.B. Finnila, M.A. Gomez, C. Sebenik, C. Stenson and J.D. Doll, Quantum annealing. A new method for minimizing multidimensional functions, Chemical Physics Letters 219 (1994) 343.

    Article  Google Scholar 

  • M.M. Fischer and S. Gopal, Artificial neural networks. A new approach to modeling interregional telecommunication flows, Journal of Regional Science 34 (1994) 503.

    Google Scholar 

  • S.T. Fischer, A note on the complexity of local search problems, Information Processing Letters 53 (1995) 69.

    Article  Google Scholar 

  • M.A. Fleischer and S.H. Jacobson, Cybernetic optimization by simulated annealing. An implementation of parallel processing using probabilistic feedback control, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • C. Fleurent and J.A. Ferland, Genetic and hybrid algorithms for graph coloring, Annals of Operations Research 63 (1996) 437.

    Google Scholar 

  • M. Flynn, Some computer organizations and their effectiveness, IEEE Transactions on Computers 21 (1972) 948.

    Google Scholar 

  • T.C. Fogarty,Evolutionary Computing, AISB Workshop, Lecture Notes in Computer Science 993 (Springer, Berlin, 1995).

    Google Scholar 

  • T.C. Fogarty,Evolutionary Computing Proceedings, Lecture Notes in Computer Science 865 (Springer, Berlin, 1994).

    Google Scholar 

  • D.B. Fogel, A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation 64 (1995) 397.

    Google Scholar 

  • D.B. Fogel, An introduction to simulated evolutionary optimization, IEEE Transactions on Neural Networks 5 (1994a) 3.

    Article  Google Scholar 

  • D.B. Fogel, Asymptotic convergence properties of genetic algorithms and evolutionary programming. Analysis and experiments, Cybernetics and Systems 25 (1994b) 389.

    Google Scholar 

  • D.B. Fogel, Applying evolutionary programming to selected traveling salesman problems, Cybernetics and Systems 24 (1993) 27.

    MathSciNet  Google Scholar 

  • D.B. Fogel and J.W. Atmar, Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems, Biological Cybernetics 63 (1990) 111.

    Google Scholar 

  • G.B. Fogel and D.B. Fogel, Continous evolutionary programming. Analysis and experiments, Cybernetics and Systems 26 (1995) 79.

    Google Scholar 

  • D.B. Fogel and L.C. Stayton, On the effectiveness of crossover in simulated evolutionary optimization, Biosystems 32 (1994) 171.

    Article  PubMed  Google Scholar 

  • L.J. Fogel, A.J. Owens and M.J. Walsh,Artificial Intelligence Through Simulated Evolution (Wiley, New York 1966).

    Google Scholar 

  • C.M. Fonseca and P.J. Fleming, An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation 3 (1995), forthcoming.

  • C.M. Fonseca and P.J. Fleming, Genetic algorithms for multiple objective optimization: formulation, discussion and generalization, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 416.

    Google Scholar 

  • S.Y. Foo, Y. Takefuji and H. Szu, Job shop scheduling based on modified Tank-Hopfield linear programming networks, Engineering Applications of Artificial Intelligence 7 (1994) 321.

    Article  Google Scholar 

  • M.A. Forbes, J.N. Holt, P.J. Kilby and A.M. Watts, BUDI. A software system for bus dispatching, Journal of the Operational Research Society 45 (1994) 497.

    Google Scholar 

  • S. Forrest,Proceedings of an International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1993).

    Google Scholar 

  • S. Forrest and M. Mitchell, What makes a problem hard for a genetic algorithm? Some results and their explanation, Machine Learning 13 (1993) 285.

    Article  Google Scholar 

  • J.C. Fort, Solving a combinatorial problem via self-organizing process. An application of the Kohonen algorithm to the traveling salesman problem, Biological Cybernetics 59 (1988) 33.

    Article  PubMed  Google Scholar 

  • P.H. Fortemps, A job shop scheduling with set up time, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 103.

    Google Scholar 

  • M. Forti, S. Manetti and M. Marini, Necessary and sufficient condition for absolute stability of neural networks, IEEE Transactions on Circuits and Systems I — Fundamental Theory and Applications 41 (1994) 491.

    Article  Google Scholar 

  • B.L. Fox, Faster simulated annealing, SIAM Journal on Optimization 5 (1995) 488.

    Article  Google Scholar 

  • B.L. Fox, Random restarting versus simulated annealing, Computers and Mathematics with Applications 27 (1994) 33.

    Article  Google Scholar 

  • B.L. Fox, Integrating and accelerating tabu search, simulated annealing, and genetic algorithms, Annals of Operations Research 41 (1993) 47.

    Article  Google Scholar 

  • B.R. Fox and M.B. McMahon, Genetic operators for sequencing problems, in:Foundations of Genetic algorithms, ed. G.J.E. Rawlings (Morgan Kaufmann, San Mateo, 1991).

    Google Scholar 

  • M.S. Fox, Constraint directed search. A case study of job shop scheduling, Working paper CMU-RI-TR-83-22, The Robotics Institute, Carnegie-Mellon University (1983).

  • M.S. Fox and S.F. Smith, ISIS. A knowledge-based system for factury scheduling, Expert Systems 1 (1984) 25.

    Google Scholar 

  • M. Foy and C. Uhrik, Exploiting domain knowledge, neural networks and genetic algorithms to harvest traffic simulation results, IFIP transactions B — Applications in Technology 11 (1993) 119.

    Google Scholar 

  • P.M. França, M. Gendreau, G. Laporte and F.M. Müller, Them-traveling salesman problem with minimax objective, Transportation Science 29 (1995) 267.

    Google Scholar 

  • J.A. Freeman,Exploring Neural Networks with Mathematics (Addison-Wesley, Wokingham, England, 1994).

    Google Scholar 

  • J.A. Freeman and D.M. Skapura,Neural Networks. Algorithms, Applications and Programming Techniques (Addison-Wesley, Wokingham, England, 1991).

    Google Scholar 

  • E.C. Freuder and R.J. Wallace, Partial constraint satisfaction, Artificial Intelligence 58 (1992) 21.

    Article  MathSciNet  Google Scholar 

  • C. Friden, A. Hertz and D. de Werra, TABARIS. An exact algorithm based on tabu search for finding a maximum independent set in a graph, Computers and Operations Research 17 (1990) 437.

    Article  Google Scholar 

  • C. Friden, A. Hertz and D. de Werra, STABULUS. A technique for finding stable sets in large graphs with tabu search, Computing 42 (1989) 35.

    Google Scholar 

  • T.L. Friesz, G. Anandalingam, N.J. Mehta, K. Nam, S.J. Shah and R.L. Tobin, The multi-objective equilibrium network design problem revisited. A simulated annealing approach, European Journal of Operational Research 65 (1993) 44.

    Article  Google Scholar 

  • T.L. Friesz, H.J. Cho, N.J. Mehta, K. Nam, R.L. Tobin and G. Anandalingam, A simulated annealing approach to the network design problem with variational inequality constraints, Transportation Science 26 (1992) 18.

    Google Scholar 

  • D. Frost and R. Dechter, Dead-end driven learning, in:Proceedings of the 12th National Conference for Artificial Intelligence (AAAI, 1994) p. 294.

  • R. Frost, SDSC Ebsa. Ensemble based simulated annealing (1995) (ftp site address: ftp.sdsc.edu,files in: /pub/sdsc/math/Ebsa).

  • J.E. Galletly, An overview of genetic algorithms, Kybernetes 21 (1992) 26.

    Google Scholar 

  • R. Gangadharan and C. Rajendran, A simulated annealing heuristic for scheduling in a flowshop with bicriteria, Computers and Industrial Engineering 27 (1994) 473.

    Article  Google Scholar 

  • B.I. Garcia, J.-Y. Potvin and J.-M. Rousseau, A parallel implementation of the tabu search heuristic for vehicle-routing problems with time window constraints, Computers and Operations Research 21 (1994) 1025.

    Article  Google Scholar 

  • M.R. Garey and D.S. Johnson,Computers and Intractability. A Guide to the Theory of NP-Completeness (Freemann, New York, 1979).

    Google Scholar 

  • A.H. Gee and R.W. Prager, Limitations of neural networks for solving traveling salesman problems, IEEE Transactions on Neural Networks 6 (1995) 280.

    Article  Google Scholar 

  • E. Gelenbe,Neural Networks. Advances and Applications (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • S.B. Gelfand and S.K. Mitter, Simulated annealing type algorithms for multivariate optimization, Algorithmica 6 (1991a) 419.

    Article  Google Scholar 

  • S.B. Gelfand and S.K. Mitter, Weak convergence of Markov-chain sampling methods and annealing algorithms to diffusions, Journal of Optimization Theory and Applications 68 (1991b) 483.

    Article  Google Scholar 

  • D.D. Gemmill, Solution to the assortment problem via the genetic algorithm, Mathematical and Computer Modelling 16 (1992) 89.

    Article  Google Scholar 

  • M. Gendreau, A. Hertz and G. Laporte, A tabu search heuristic for the vehicle routing problem, Management Science 40 (1994) 1276.

    Google Scholar 

  • M. Gendreau, G. Laporte and J.-Y. Potvin, Vehicle routing 1. Metaheuristics, in:Local Search in Combinatorial Optimization, ed. E.H.L. Aarts and J.K. Lenstra (Wiley, Chichester, 1996), forthcoming.

    Google Scholar 

  • M. Gendreau, G. Laporte and R. Séguin, A tabu search heuristic for the vehicle routing problem with stochastic demands and customers, Operations Research (1996), forthcoming.

  • M. Gendreau, P. Soriano and L. Salvail, Solving the maximum clique problem using a tabu search approach, Annals of Operations Research 41 (1993) 385.

    Article  Google Scholar 

  • J.A. George, J.M. George and B.W. Lamar, Packing different-sized circles into a rectangular container, European Journal of Operational Research 84 (1995) 693.

    Article  Google Scholar 

  • H. Ghaziri, Solving routing problems by a self-organizing feature map, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • H. Ghaziri, Supervision in the self-organizing feature map: Application to the vehicle routing problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • M. Ghosh, S.S. Manna and B.K. Chakrabarti, The traveling salesman problem on a dilute lattice. A simulated annealing study, Journal of Physics A — Mathematical and General 21 (1988) 1483.

    Article  Google Scholar 

  • J.C. Gilkinson, L.C. Rabelo and B.O. Bush, A real-world scheduling problem using genetic algorithm, Computers and Industrial Engineering 29 (1995) 177.

    Article  Google Scholar 

  • G.C. Gini and C. Rogialli, CONSTRUCTOR. A constraint-based language, Computer Systems Science and Engineering 9 (1994) 255.

    Google Scholar 

  • R.S. Ginsberg, Dynamic backtracking, Journal of Artificial Intelligence Research 1 (1993) 25.

    Google Scholar 

  • L. Gislen, C. Peterson and B. Soderberg, Teachers and classes with neural networks, International Journal of Neural Systems 1 (1989) 167.

    Article  Google Scholar 

  • C.A. Glass, C.N. Potts and P. Shade, Unrelated parallel machine scheduling using local search, Mathematical and Computer Modelling 20 (1994) 41.

    Article  Google Scholar 

  • C.A. Glass and C.N. Potts, A comparison of local search methods for flow shop scheduling, Annals of Operations Research 63 (1996) 489.

    Google Scholar 

  • F. Glover, Multilevel tabu search and embedded search neighbourhoods for the travelling salesman problem, ORSA Journal on Computing (1996), forthcoming.

  • F. Glover, Tabu search fundamentals and uses, Working paper, Graduate School of Business, University of Colorado, Boulder (1995a).

    Google Scholar 

  • F. Glover, Tabu thresholding. Improved search by non-monotonic trajectories, ORSA Journal on Computing 7 (1995b) 426.

    Google Scholar 

  • F. Glover, Scatter search and star-paths. Beyond the genetic metaphor, OR Spektrum 17 (1995c) 125.

    Article  Google Scholar 

  • F. Glover, Optimization by ghost image processes in neural networks, Computers and Operations Research 21 (1994a) 801.

    Article  MathSciNet  Google Scholar 

  • F. Glover, Tabu search for nonlinear and parametric optimization (with links to genetic algorithms), Discrete Applied Mathematics 49 (1994b) 231.

    Article  MathSciNet  Google Scholar 

  • F. Glover, Tabu search. Improved solution alternatives, in:Mathematical Programming State of the Art, ed. J.R. Birge and K.G. Murty (University of Michigan Press, Ann Arbor, 1994c).

    Google Scholar 

  • F. Glover, Genetic algorithms and scatter search. Unexpected potentials, Statistics and Computing 4 (1994d) 131.

    Google Scholar 

  • F. Glover, New ejection chain and alternating path methods for traveling salesman problems, in:Operations Research and Computer Science. New Developments in Their Interfaces, ed. O. Balci, R. Shardi and S.A. Zenios (Pergamon Press, Oxford, 1992).

    Google Scholar 

  • F. Glover, Artificial intelligence, heuristic frameworks and tabu search, Managerial and Decision Economics 11 (1990a) 365.

    Google Scholar 

  • F. Glover, Tabu search. A tutorial, Interfaces 20 (1990b) 74.

    Google Scholar 

  • F. Glover, Tabu search. Part II, ORSA Journal on Computing 2 (1990c) 4.

    Google Scholar 

  • F. Glover, Tabu search. Part I, ORSA Journal on Computing 1 (1989) 190.

    Google Scholar 

  • F. Glover, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research 13 (1986) 533.

    Article  Google Scholar 

  • F. Glover, Heuristics for integer programming using surrogate constraints, Decision Sciences 8 (1977) 156.

    Google Scholar 

  • F. Glover and H.J. Greenberg, New approaches for heuristic search. A bilateral linkage with artificial intelligence, European Journal of Operational Research 39 (1989) 119.

    Article  Google Scholar 

  • F. Glover, J.P. Kelly and M. Laguna, Genetic algorithms and tabu search. Hybrids for optimization, Computers and Operations Research 22 (1995) 111.

    Article  Google Scholar 

  • F. Glover and G.A. Kochenberger, Critical event tabu search for multidimensional knapsack problems, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • F. Glover, M. Lee and J. Ryan, Least-cost network topology design for a new service. An application of tabu search, Annals of Operations Research 33 (1991) 351.

    Article  Google Scholar 

  • F. Glover and M. Laguna, Tabu search, in:Modern Heuristic Techniques for Combinatorial Problems, ed. C.C. Reeves (Blackwell, Oxford, 1993).

    Google Scholar 

  • F. Glover and M. Laguna, Target analysis to improve a tabu search method for machine scheduling, Arabian Journal for Science and Engineering 16 (1991) 239.

    Google Scholar 

  • F. Glover, M. Laguna, E.D. Taillard and D. de Werra,Tabu Search, Annals of Operations Research 43 (Baltzer, Basel, 1993).

    Google Scholar 

  • F. Glover and C. McMillan, The general employee scheduling problem. An integration of MS and AI, Computers and Operations Research 13 (1986) 563.

    Article  Google Scholar 

  • F. Glover, J.M. Mulvey and K. Hoyland, Solving dynamic stochastic control problems in finance using tabu search with variable scaling, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • F. Glover and E. Pesch, TSP ejection chain, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).

    Google Scholar 

  • F. Glover, E. Pesch and I.H. Osman, Efficient facility layout planning, Graduate School of Business, University of Colorado, Boulder (1995).

    Google Scholar 

  • F. Glover and J. Skorin-Kapov, Heuristic advances in optimization integrating tabu search, ejection chains and neural networks, Working paper, Graduate School of Business, University of Colorado, Boulder (1993).

    Google Scholar 

  • F. Glover, E.D. Taillard and D. de Werra, A user's guide to tabu search, Annals of Operations Research 41 (1993) 3.

    Article  Google Scholar 

  • F. Glover, J. Xu and S.Y. Chiu, Designing a tabu search approach for the Steiner tree star problem, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).

    Google Scholar 

  • W.L. Goffe, G.D. Ferrier and J. Rogers, Global optimization of statistical functions with simulated annealing, Journal of Econometrics 60 (1994) 65.

    Article  Google Scholar 

  • W.L. Goffe, G.D. Ferrier and J. Rogers, Simulated annealing. An initial application in econometrics, Computer Science in Economics and Management 5(1992) 113.

    Article  Google Scholar 

  • D.E. Goldberg, A note on Boltzmann tournament selection for genetic algorithms and population oriented simulated annealing, Complex Systems 4 (1990) 445.

    Google Scholar 

  • D.E. Goldberg,Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Wokingham, England, 1989).

    Google Scholar 

  • D.E. Goldberg, K. Deb and J.H. Clark, Genetic algorithms, noise and the sizing of populations, Complex Systems 6 (1992) 333.

    Google Scholar 

  • B.L. Golden, Charting new directions in OR and CS, ORSA Journal on Computing 6 (1994) 107.

    Google Scholar 

  • B.L. Golden and C.C. Skiscim, Using simulated annealing to solve routing and location-problems, Naval Research Logistics 33 (1986) 261.

    MathSciNet  Google Scholar 

  • B.L. Golden and W. Stewart, Empirical analysis of heuristics, in:The Traveling Salesman Problem. A Guided Tour of Combinatorial Oprimization, ed. E. Lawler, J. Lenstra, A. Rinnooy Kan and D. Shmoys (Wiley, New York 1985).

    Google Scholar 

  • M. Goldstein, Self-organizing feature maps for the multiple traveling salesman problem, in:Proceedings of the IEEE International Neural Network Conference (Paris, 1990) p. 258.

  • V.S. Gordon and D. Whitley, Serial and parallel genetic algorithms as function optimizers, in:Proceedings of the International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 177.

    Google Scholar 

  • M. Gorges-Schleuter, Explicit parallelism of genetic algorithms through population structures,Lecture Notes in Computer Science 496 (1991) p. 150.

    Google Scholar 

  • V. Granville, M. Krivanek and J.P. Rasson, Simulated annealing. A proof of convergence, IEEE Transactions On Pattern Analysis and Machine Intelligence 16 (1994) 652.

    Article  Google Scholar 

  • H. Greenberg, Computational testing. Why, how and how much, ORSA Journal on Computing 2 (1990) 7.

    Google Scholar 

  • J.W. Greene and K.J. Supowit, Simulated annealing without rejected moves, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 5 (1986) 221.

    Article  Google Scholar 

  • D.R. Greening, Parallel simulated annealing techniques, Physica D42 (1990) 293.

    Google Scholar 

  • R.N. Greenwell, J.E. Angus and M. Finck, Optimal mutation probability for genetic algorithms, Mathematical and Computer Modelling 21 (1995) 1.

    Article  Google Scholar 

  • J.J. Grefenstette,Genetic Algorithms for Machine Learning (Kluwer, Boston, 1994).

    Google Scholar 

  • J.J. Grefenstette, Genetic algorithms, IEEE Expert-Intelligent Systems and Their Applications 8 (1993) 5.

    Google Scholar 

  • J.J. Grefenstette, Incorporating problem specific knowledge into genetic algorithms, in:Genetic Algorithms and Simulated Annealing, ed. L. Davis (Pitman, London, 1987).

    Google Scholar 

  • J.J. Grefenstette, A user's guide to GENESIS, Working paper, Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington DC (1987a).

    Google Scholar 

  • J.J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 16 (1986) 122.

    Google Scholar 

  • J.J. Grefenstette,Proceedings of an International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1985).

    Google Scholar 

  • J.J. Grefenstette, GENESIS. A system for using genetic search procedure, in:Proceedings of the 1984 Conference on Intelligent Systems and Machines (1984) p. 161.

  • P. Greistorfer, Computational experiments with heuristics for a capacitated arc routing problem, Working paper, Department of Business, University of Graz, Austria (1994).

    Google Scholar 

  • A. Grigoriev, Artificial intelligence or stochastic relaxation. Simulated annealing challenge in heuristic programming, in:Artificial Intelligence 2, ed. D. Levy and D. Beal (Ellis Horwood, Hertfordshire, England, 1991).

    Google Scholar 

  • F. Gruau, Automatic definition of modular neural networks, Adaptive Behavior 3 (1994) 151.

    Google Scholar 

  • J. Gu, Local search for satisfiability (SAT) problem, IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 1108.

    Google Scholar 

  • J. Gu, Design of efficient local search algorithms,Lecture Notes in Artificial Intelligence 604 (1992) p. 651.

    Google Scholar 

  • J. Gu and X.F. Huang, Efficient local search with search space smoothing. A case-study of the traveling salesman problem (TSP), IEEE Transactions on Systems, Man and Cybernetics 24 (1994) 728.

    Google Scholar 

  • J. Gu, X.F. Huang and B. Du, A quantitative solution to constraint satisfaction problem (CSP), New Generation Computing 13 (1994) 99.

    Google Scholar 

  • A. Guinet, Scheduling independent jobs on uniform parallel machines to minimize tardiness criteria, Journal of Intelligent Manufacturing 6 (1995) 95.

    Article  Google Scholar 

  • L.F. Gulyanitskii, L.B. Koshlai and I.V. Sergienko, Convergence of a simulation method for solution of combinatorial optimization problems, Cybernetics and Systems Analysis 29 (1993) 445.

    Article  Google Scholar 

  • A. Gupta and M.S. Lam, Estimating missing values using neural networks, Journal of the Operational Research Society 47 (1996) 229.

    Google Scholar 

  • D.K. Gupta, An enhancement scheme for constraint satisfaction problems (CSPS), International Journal of Computer Mathematics 47 (1993) 177.

    Google Scholar 

  • M.C. Gupta, Y.P. Gupta and A. Kumar, Minimizing flow time variance in a single-machine system using genetic algorithms, European Journal of Operational Research 70 (1993) 289.

    Article  Google Scholar 

  • V. Gupta and E. Schenfeld, Annealed embeddings of communication patterns in an interconnection cached network, IEEE Transactions on Parallel and Distributed Systems 6 (1995) 1153.

    Article  Google Scholar 

  • Y.P. Gupta, M.C. Gupta, A. Kumar and C. Sundram, Minimizing total intercell and intracell moves in cellular manufacturing. Genetic algorithm approach, International Journal of Computer Integrated Manufacturing 8 (1995) 92.

    Google Scholar 

  • J. Haddock and J. Mittenthal, Simulation optimization using simulated annealing, Computers and Industrial Engineering 22 (1992) 387.

    Article  Google Scholar 

  • B. Hajek and G. Sasaki, Simulated annealing. To cool or not?, Systems and Control Letters 12 (1989) 443.

    Article  Google Scholar 

  • B. Hajek, Cooling schedules for optimal annealing, Mathematics of Operations Research 13 (1988) 311.

    Google Scholar 

  • P. Hajela and C.Y. Lin, Genetic algorithms in optimization problems with discrete and integer design variables, Engineering Optimization 19 (1992) 309.

    Google Scholar 

  • T. Hamada, C.K. Cheng and P.M. Chau, An efficient multilevel placement technique using hierarchical partitioning, IEEE Transactions on Circuits and Systems I — Fundamental Theory and Applications 39 (1992) 432.

    Article  Google Scholar 

  • B.T. Han, G. Diehr and J.S. Cook, Multipletype, two-dimensional bin packing problems. Applications and algorithms, Annals of Operations Research 50 (1994) 239.

    Article  MathSciNet  Google Scholar 

  • M.M. Han, S. Tatsumi, Y. Kitamura and T. Okumoto, Parallel genetic algorithms based on a multiprocessor system fin and its application, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E78A (1995) 1595.

    Google Scholar 

  • S. Hanafi, A. Fréville and A. El-Abdellaoui, Comparison of heuristics for the 0–1 multidimensional knapsack problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • P. Hansen, The steepest ascent mildest descent heuristic for combinatorial programming, Presented at the Congress on Numerical Methods in Combinatorial Optimization, Capri (1986).

  • P. Hansen, E.D. Pedrosa and C.C. Ribeiro, Location and sizing of offshore platforms for oil-exploration, European Journal of Operational Research 58 (1992) 202.

    Article  Google Scholar 

  • P. Hansen and B. Jaumard, Algorithms for the maximum satisfiability problem, Computing 44 (1990) 279.

    MathSciNet  Google Scholar 

  • P. Hansen and K.-W. Lih, Heuristic reliability optimization by tabu search, Annals of Operations Research 63 (1996) 321.

    Google Scholar 

  • O. Hansson and A. Mayer, Decision-theoretic control of constraint satisfaction and scheduling, in:Intelligent Scheduling Systems, ed. D.E. Brown and W.T. Scherer,Operations Research/Computer Science Interfaces 3 (Kluwer, Boston, 1995).

    Google Scholar 

  • J.K. Hao, A clausal genetic representation and its related evolutionary procedures for satisfability problems, in:Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA'95 (Springer, Berlin, 1995) p. 289.

    Google Scholar 

  • J.K. Hao and R. Dorne, An empirical comparison of two evolutionary methods for satisfiability problems, in:Proceedings of the IEEE International Conference on Evolutionary Computation (Orlando, Florida, 1994).

    Google Scholar 

  • J.K. Hao and R. Dorne, Study of genetic search for the frequency assignment problem,Lecture Notes in Computer Science (Springer, Berlin, 1995), forthcoming.

    Google Scholar 

  • B.L.M. Happel and J.M.J. Murre, Design and evolution of modular neural-network architectures, Neural Networks 7 (1994) 985.

    Google Scholar 

  • X. Hardyao, Finding approximate solutions to NP-hard problems by neural networks is hard, Information Processing Letters 41 (1992) 93.

    Article  Google Scholar 

  • D. Harel and M. Sardas, Randomized graph drawing with heavy-duty preprocessing, Journal of Visual Languages and Computing 6 (1995) 233.

    Article  Google Scholar 

  • G. Harhalakis, J.M. Proth and X.L. Xie, Manufacturing cell design using simulated annealing. An industrial application, Journal of Intelligent Manufacturing 1 (1990) 185.

    Article  Google Scholar 

  • J.P. Hart and A.W. Shogan, Semi-greedy heuristics. An empirical study, Operations Research Letters 6 (1987) 107.

    Article  MathSciNet  Google Scholar 

  • S.M. Hart and C.L.S. Chen, Simulated annealing and the mapping problem. A computational study, Computers and Operations Research 21 (1994) 455.

    Article  Google Scholar 

  • M. Hasan and I.H. Osman, Local search algorithms for the maximal planar layout problem, International Transactions in Operational Research 2 (1995) 89.

    Article  Google Scholar 

  • S. Hashiba and T.C. Chang, Heuristic and simulated annealing approaches to PCB assembly setup reduction, IFIP Transactions B — Applications in Technology 3 (1992) 769.

    Google Scholar 

  • S. Haykin,Neural Networks. A Comprehensive Foundation (MacMillan, New York, 1994).

    Google Scholar 

  • P. Healy and R. Moll, A new extension of local search applied to the dial-a-ride problem, European Journal of Operational Research 83 (1995) 83.

    Article  Google Scholar 

  • R. Heckmann and T. Lengauer, A simulated annealing approach to the nesting problem in the textile manufacturing industry, Annals of Operations Research 57 (1995) 103.

    Article  Google Scholar 

  • J. Heistermann, Application of a genetic approach as an algorithm for neural networks,Lecture Notes in Computer Science 496 (1991) p. 297.

    Google Scholar 

  • B.J. Hellstrom and L.N. Kanal, Asymmetric mean-field neural networks for multiprocessor scheduling, Neural Networks 5 (1992) 671.

    Google Scholar 

  • S.S. Heragu, Modeling the machine layout problem, Computers and Industrial Engineering 19 (1990) 294.

    Article  Google Scholar 

  • S.S. Heragu and A.S. Alfa, Experimental analysis of simulated annealing based algorithms for the layout problem, European Journal of Operational Research 57 (1992) 190.

    Article  Google Scholar 

  • S.S. Heragu and A.S. Alfa, A simulated annealing based approach to solve the facility layout problem, in:Proceedings of the 4th Advanced Technology Conference (U.S. Postal Service, Washington 1990) p. 489.

    Google Scholar 

  • S.S. Heragu and B. Mazacioglu, Variations of the simulated annealing algorithm applied to the order picking problem, Working paper 37-91-295, Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York (1991).

    Google Scholar 

  • L. Hérault, Réseaux de neurons récursifs pour l'optimisation combinatoire: Application à la théorie des graphes et à la vision par ordinateur, Ph.D. Thesis, Institut National Polytechnique de Grenoble, France (1991).

    Google Scholar 

  • L. Hérault and J.J. Niez, Neural network and combinatorial optimization. A study of NP-complete graph problems, in:Neural Networks. Advances and Applications, ed. E. Gelenbe (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • L. Hérault and J.J. Niez, Neural networks and graphK-partitioning, Complex Systems 3 (1989) 521.

    Google Scholar 

  • E. Herbert and K.A. Dowsland, A family of genetic algorithms for the pallet loading problem, Annals of Operations Research 63 (1996) 415.

    Google Scholar 

  • J.W. Hermann and C.Y. Lee, Solving a class scheduling problem with genetic algorithm, ORSA Journal on Computing 7 (1995) 443.

    Google Scholar 

  • A. Hertz, Finding a feasible course schedule using tabu search, Discrete Applied Mathematics 35 (1992) 255.

    Article  Google Scholar 

  • A. Hertz, Tabu search for large scale timetabling problems, European Journal of Operational Research 54 (1991) 39.

    Article  Google Scholar 

  • A. Hertz, B. Jaumard and M. Poggi de Aragao, Local optima topology for thek-coloring problem, Discrete Applied Mathematics 49 (1994) 257.

    Article  Google Scholar 

  • A. Hertz, B. Jaumard, C.C. Ribeiro and W.P. Formosinho, A multicriteria tabu search approach to cell formation problems in group technology with multiple objectives, RAIRO — Operations Research 28 (1994) 303.

    Google Scholar 

  • A. Hertz, E.D. Taillard and D. de Werra, Tabu search, in:Local Search in Optimization, ed. E.H.L. Aarts and J.K. Lenstra (Wiley, Chichester 1996), forthcoming.

    Google Scholar 

  • A. Hertz and D. de Werra, The tabu search metaheuristic. How we used it, Annals of Mathematics and Artificial Intelligence 1 (1990) 111.

    Article  Google Scholar 

  • A. Hertz and D. de Werra, Using tabu search techniques for graph-coloring, Computing 39 (1987) 345.

    Google Scholar 

  • J. Hertz, A. Krogh and R.G. Palmer,Introduction to the Theory of Neural Computation (Addison-Wesley, Wokingham, England, 1991).

    Google Scholar 

  • T.M. Heskes, E.T.P. Slijpen and B. Kappen, Cooling schedules for learning in neural networks, Physical Review E47 (1993) 4457.

    Google Scholar 

  • J. Hesser, R. Männer and O. Stucky, On Steiner trees and genetic algorithms,Lecture Notes in Artificial Intelligence 565 (1991) p. 509.

    Google Scholar 

  • D.B. Hibbert, Genetic algorithms in chemistry, Chemometrics and Intelligent Laboratory Systems 19 (1993) 277.

    Article  Google Scholar 

  • T. Hill, L. Marquez, M. O'Connor and W. Remus, Artificial neural-network models for forecasting and decision making, International Journal of Forecasting 10 (1994) 5.

    Article  Google Scholar 

  • K.S. Hindi, Solving the CLSP by a tabu search heuristic, Journal of the Operational Research Society 47 (1996) 151.

    Google Scholar 

  • K.S. Hindi, Solving the single-item, capacitated dynamic lot-sizing problem with startup and reservation costs by tabu search, Computers and Industrial Engineering 28 (1995) 701.

    Article  Google Scholar 

  • K.S. Hindi and E. Toczylowski, Detailed scheduling of batch-production in a cell with parallel facilities and common renewable resources, Computers and Industrial Engineering 28 (1995) 839.

    Article  Google Scholar 

  • K.S. Hindi and Y.M. Hamam, Solving the part families problem in discrete-parts manufacture by simulated annealing, Production Planning and Control 5 (1994) 160.

    Google Scholar 

  • D.T. Hiquebran, A.S. Alfa, J.A. Shapiro and D.H. Gittoes, A revised simulated annealing and cluster-1st route-2nd algorithm applied to the vehicle routing problem, Engineering Optimization 22 (1994) 77.

    Google Scholar 

  • C.A. Hjorring, The vehicle routing problem and local search meta-heuristics, Ph.D. Thesis, Department of Engineering Science, The University of Auckland, NZ (1995).

    Google Scholar 

  • K.H. Hoffmann, M. Christoph and M. Hanf, Optimizing simulated annealing,Lecture Notes in Computer Science 496 (1991) p. 221.

    Google Scholar 

  • K.H. Hoffmann and P. Salamon, The optimal simulated annealing schedule for a simple model, Journal of Physics A — Mathematical and General 23 (1990) 3511.

    Article  Google Scholar 

  • K.H. Hoffmann, D. Wurtz, C. Degroot and M. Hanf, Concepts in optimizing simulated annealing schedules. An adaptive approach for parallel and vector machines,Lecture Notes in Economics and Mathematical Systems 367 (1991) p. 155.

    Google Scholar 

  • F. Hoffmeister and T. Bäck, Genetic algorithms and evolution strategies. Similarities and differences,Lecture Notes in Computer Science 496 (1991) p. 455.

    Google Scholar 

  • J. Hofmann and C. Schiemangk, Placement heuristics for generation of FMS layouts,Lecture Notes in Control and Information Sciences 143 (1990) p. 780.

    Google Scholar 

  • J.H. Holland,Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, 1992a).

    Google Scholar 

  • J.H. Holland, Genetic algorithms, Scientific American 267 (1992b) 66.

    Google Scholar 

  • C.W. Holsapple, V.S. Jacob, R. Pakath and J.S. Zaveri, A genetics based hybrid scheduler for generating static schedules in flexible manufacturing contexts, IEEE Transactions on Systems, Man, and Cybernetics 23 (1993) 953.

    Google Scholar 

  • A. Homaifar, C.X. Qi and S.H. Lai, Constrained optimization via genetic algorithms, Simulation 62 (1994) 242.

    Google Scholar 

  • A. Homaifar, S. Guan and G. Liepins, A new approach on the traveling salesman problem by genetic algorithms, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 460.

    Google Scholar 

  • C.E. Hong and B.M. McMillin, Relaxing synchronization in distributed simulated annealing, IEEE Transactions on Parallel and Distributed Systems 6 (1995) 189.

    Article  Google Scholar 

  • G. Hong, M. Zuckermann, R. Harris and M. Grant, A fast algorithm for simulated annealing, Physica Scripta T38 (1991) 40.

    Google Scholar 

  • J.N. Hooker, Testing heuristics. We have it all wrong, Journal of Heuristics 1 (1995) 33.

    Google Scholar 

  • J.N. Hooker, Needed. An empirical science of algorithms, Operations Research 42 (1994) 201.

    Google Scholar 

  • J.N. Hooker and N.R. Natraj, Solving general routing and scheduling problem by chain decomposition and tabu search, Transportation Science 29 (1995) 30.

    Google Scholar 

  • P.M. Hooper, Nearly orthogonal randomized designs, Journal of the Royal Statistical Society Series B — Methodological 55 (1993) 221.

    Google Scholar 

  • J.J. Hopfield and D. Tank, Computing with neural circuits. A model, Science 233 (1986) 624.

    Google Scholar 

  • J.J. Hopfield and D. Tank, Neural computations of decisions in optimization problems, Biological Cybernetics 52 (1985) 141.

    PubMed  Google Scholar 

  • U. Horchner and J.H. Kalivas, Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection, Analytica Chimica Acta 311 (1995) 1.

    Article  Google Scholar 

  • J. Horn, N. Nafpliotis and D.E. Goldberg, A niched Pareto genetic algorithm for multiobjective optimization, in:Proceedings of the 1st IEEE Conference on Evolutionary Computation (1994) p. 82.

  • J.T. Horng and B.J. Liu, Extending SQL with graph matching, set covering and partitioning, Journal of the Chinese Institute of Engineers 17 (1994) 13.

    Google Scholar 

  • E.S.H. Hou, N. Ansari and H. Ren, A genetic algorithm for multiprocessor scheduling, IEEE Transactions on Parallel and Distributed Systems 5 (1994) 113.

    Article  Google Scholar 

  • T.C. Hu, A.B. Khang and C.-W.A. Tsao, Old bachelor acceptance. A new class of non-monotonic threshold accepting methods, ORSA Journal on Computing 7 (1995) 417.

    Google Scholar 

  • N.F. Hu, Tabu search method with random moves for globally optimal design, International Journal for Numerical Methods in Engineering 35 (1992) 1055.

    Google Scholar 

  • S.H. Huang and H.C. Zhang, Artificial neural networks in manufacturing. Concepts, applications, and perspectives, IEEE Transactions on Components Packaging and Manufacturing Technology Part A 17 (1994) 212.

    Article  Google Scholar 

  • T. Huang, C. Zhang, S. Lee and H.P. Wang, Implementation and comparison of 3 neural network learning algorithms, Kybernetes 22 (1993) 22.

    Google Scholar 

  • W.-C. Huang and C.-Y. Kao, A genetic algorithm approach for set covering problems, Working paper, Department of Computer Science and Information Engineering, National Taiwan University, Taipei (1992).

    Google Scholar 

  • M.L. Huber, Structural optimization of vapor-pressure correlations using simulated annealing and threshold accepting. Application to R134A, Computers and Chemical Engineering 18 (1994) 929.

    Article  Google Scholar 

  • R. Hubscher and F. Glover, Applying tabu search with influential diversification to multiprocessor scheduling, Computers and Operations Research 21 (1994) 877.

    Article  Google Scholar 

  • A. Hunter, Sugal V2.1, SUnderland Genetic Algorithms Library, University of Sunderland, England (1996) (ftp site address: osiris@sund.ac.uk,files in: /pub/sugal).

    Google Scholar 

  • C.L. Huntley and D.E. Brown, A parallel heuristic for quadratic assignment problems, Computers and Operations Research 18 (1991) 275.

    Article  Google Scholar 

  • J. Hurink, B. Jurisch and M. Thole, Tabu search for the job-shop scheduling problem with multipurpose machines, OR Spektrum 15 (1994) 205.

    Article  Google Scholar 

  • P. Husbands, An ecosystems model for integrated production planning, The International Journal of Computer Integrated Manufacturing 6 (1993) 74.

    Google Scholar 

  • P. Husbands, F. Mill and S. Warrington, Genetic algorithms, production plan optimization and scheduling,Lecture Notes in Computer Science 496 (1991) p. 80.

    Google Scholar 

  • Y. Ichikawa and T. Sawa, Neural network application for direct feedback controllers, IEEE Transactions on Neural Networks 3 (1992) 224.

    Article  Google Scholar 

  • O. Icmeli and S.S. Erenguc, A tabu search procedure for the resource constrained project scheduling problem with discounted cash flows, Computers and Operations Research (1994) 841.

  • H. Igarashi, A solution for combinational optimization problems using a 2-layer random-field model. Mean-field approximation, Systems and Computers in Japan 25 (1994) 61.

    Google Scholar 

  • ILOG: ILOG Solver, Schedule, Collected papers, ILOG Headquarters, Gentilly, France (1994).

    Google Scholar 

  • L. Ingber, ASA. Adaptive simulated annealing, (1995) (ftp site: ftp.alumi.caltech.edu,files in: /pub/ingber).

  • L. Ingber, Simulated annealing. Practice versus theory, Mathematical and Computer Modelling 18 (1993) 29.

    Article  MathSciNet  Google Scholar 

  • L. Ingber, Very fast simulated re-annealing, Mathematical and Computer Modelling 12 (1989) 967.

    Article  Google Scholar 

  • L. Ingber, H. Fujio and M.F. Wehner, Mathematical comparison of combat computer models to exercise data, Mathematical and Computer Modelling 15 (1991) 65.

    Article  Google Scholar 

  • L. Ingber and B. Rosen, Genetic algorithms and very fast simulated re-annealing. A comparison, Mathematical and Computer Modelling 16 (1992) 87.

    Article  MathSciNet  Google Scholar 

  • L. Ingber, M.F. Wehner, G.M. Jabbour and T.M. Barnhill, Application of statistical mechanics methodology to term structure bond-pricing models, Mathematical and Computer Modelling 15 (1991) 77.

    Article  Google Scholar 

  • H. Ishibuchi, S. Misaki and H. Tanaka, Modified simulated annealing algorithms for the flow-shop sequencing problem, European Journal of Operational Research 81 (1995) 388.

    Article  Google Scholar 

  • H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka, Selection of fuzzy if-then rules by a genetic method, Electronics and Communications in Japan Part III — Fundamental Electronic Science 77 (1994) 94.

    Google Scholar 

  • H. Ishibuchi, N. Yamamoto, S. Misaki and H. Tanaka, Local search algorithms for flow-shop scheduling with fuzzy due-dates, International Journal of Production Economics 33 (1994) 53.

    Article  Google Scholar 

  • H. Ishibuchi, N. Yamamoto, T. Murata and H. Tanaka, Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems, Fuzzy Sets and Systems 67 (1994) 81.

    Article  MathSciNet  Google Scholar 

  • M. Ishikawa, T. Toya, M. Hoshida, K. Nitta, A. Ogiwara and M. Kanehisa, Multiple sequence alignment by parallel simulated annealing, Computer Applications in the Biosciences 9 (1993) 267.

    PubMed  Google Scholar 

  • R. Jackson, P. Boggs, S. Nash and S. Powell, Report of the ad hoc committee to revise the guidelines for reporting computational experiments in mathematical programming, Mathematical Programming 49 (1990) 413.

    Article  Google Scholar 

  • L.W. Jacobs and M.J. Brusco, A local-search heuristic for large set-covering problems, Naval Research Logistics 42 (1995) 1129.

    MathSciNet  Google Scholar 

  • S.H. Jacobson, How difficult is the frequency selection problem, Operations Research Letters 17 (1995) 139.

    Article  Google Scholar 

  • A. Jagota, Approximating maximum clique with a Hopfield network, IEEE Transactions on Neural Networks 6 (1995) 724.

    Article  Google Scholar 

  • L.C. Jain, Hybrid connectionist systems in research and teaching, IEEE Aerospace and Electronic Systems Magazine 10 (1995) 14.

    Article  Google Scholar 

  • S. Jajodia, I. Minis, G. Harhalakis and J.M. Proth, CLASS. Computerized layout solutions using simulated annealing, International Journal of Production Research 30 (1992) 95.

    Google Scholar 

  • H. James, Software for studying and developing applications of artificial neural networks, Economic Journal 104 (1994) 181.

    Google Scholar 

  • M. Jampel, Constraint logic programming. A bibliography, Working paper, City University, London (1995).

    Google Scholar 

  • C.Z. Janikow, A knowledge-intensive genetic algorithm for supervised learning, Machine Learning 13 (1993) 189.

    Article  Google Scholar 

  • J. Jansen, R.C.M.H. Douven and E.E.M Vanberkum, An annealing algorithm for searching optimal block-designs, Biometrical Journal 34 (1992) 529.

    MathSciNet  Google Scholar 

  • G.K. Janssens and A. van Breedam, A simulated annealing postprocessor for the vehicle routing problem, in:Applications of Modern Heuristic Methods, ed. V. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995).

    Google Scholar 

  • B. Jaumard, P.S. Ow and B. Simeone, A selected artificial intelligence bibliography for operations researchers, Annals of Operations Research 12 (1988) 1.

    Google Scholar 

  • C. Jedrzejek and L. Cieplinski, Heuristic versus statistical physics approach to optimization problems, Acta Physica Polonica B 26 (1995) 977.

    Google Scholar 

  • D.E. Jeffcoat and R.L. Bulfin, Simulated annealing for resource-constrained scheduling, European Journal of Operational Research 70 (1993) 43.

    Article  Google Scholar 

  • W. Jeffrey and R. Rosner, Optimization algorithms. Simulated annealing and neural network processing, Astrophysical Journal 310 (1986) 473.

    Article  Google Scholar 

  • C.S. Jeong and M.H. Kim, Fast parallel simulated annealing for traveling salesman problem on SIMD-machines with linear interconnections, Parallel Computing 17 (1991) 221.

    Article  Google Scholar 

  • L.M. Jin and S.P. Chan, A genetic approach for network partitioning, International Journal of Computer Mathematics 42 (1992a) 47.

    Google Scholar 

  • L.M. Jin and S.P. Chan, Analog placement by formulation of macro-components and genetic partitioning, International Journal of Electronics 73 (1992b) 157.

    Google Scholar 

  • S. Jockusch and H. Ritter, Self-organizing maps. Local competition and evolutionary optimization, Neural Networks 7 (1994) 1229.

    Article  Google Scholar 

  • P. Jog, J.Y. Suh and D. van Gucht, Parallel genetic algorithms applied to the travelling salesman problem, SIAM Journal on Optimization 1 (1991) 515.

    Article  Google Scholar 

  • D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon, Optimization by simulated annealing. An experimental evaluation 2. Graph-coloring and number partitioning, Operations Research 39 (1991) 378.

    Google Scholar 

  • D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon, Optimization by simulated annealing. An experimental evaluation 1. Graph partitioning, Operations Research 37 (1989) 865.

    Google Scholar 

  • D.S. Johnson and L.A. McGeoch, The travelling salesman problem. A case study in local search, in:Local Search in Optimization, ed. E.H.L. Aarts and J.K. Lenstra (Wiley, Chichester, 1996), forthcoming.

    Google Scholar 

  • D.S. Johnson, C. Papadimitriou and M. Yannakis, How easy is local search, Journal of Computer and System Sciences 37 (1988) 79.

    Article  Google Scholar 

  • D.S. Johnson and C. Papadimitriou, Performance guarantees for heuristics, in:The Traveling Salesman Problem. A Guided Tour of Combinatorial Optimization, ed. E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan and D.B. Shmoys (Wiley, New York, 1985).

    Google Scholar 

  • M.D. Johnston and H.M. Adorf, Scheduling with neural networks. The case of the Hubble space telescope, Computers and Operations Research 19 (1992) 209.

    Article  Google Scholar 

  • A. Jones, L. Rabelo and Y.W. Yih, A hybrid approach for real-time sequencing and scheduling, International Journal of Computer Integrated Manufacturing 8 (1995) 145.

    Google Scholar 

  • A.E.W. Jones and G.W. Forbes, An adaptive simulated annealing algorithm for global optimization over continuous-variables, Journal of Global Optimization 6 (1995) 1.

    Article  Google Scholar 

  • A.J. Jones, Genetic algorithms and their applications to the design of neural networks, Neural Computing and Applications 1 (1993) 32.

    Article  Google Scholar 

  • D.R. Jones and M.A. Beltramo, Solving partitioning problems with genetic algorithms, in:Proceedings of the 4th International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1991) p. 442.

    Google Scholar 

  • G. Jones, A.M. Robertson and P. Willett, An introduction to genetic algorithms and to their use in information retrieval, Online and CDROM Review 18 (1994) 3.

    Google Scholar 

  • R.M. Jorgensen, H. Thomsen and R.V.V. Vidal, The afforestation problem. A heuristic method based on simulated annealing, European Journal of Operational Research 56 (1992) 184.

    Article  Google Scholar 

  • K. Jörnsten and A. Løkketangen, Tabu search for weightedK-cardinality trees, Working paper M9303, Institute of Informatics, Molde College, Molde, Norway (1993).

    Google Scholar 

  • J. Jszefowska, G. Waligsra and J. Weglarz, A tabu search algorithm for some discrete-continuous scheduling problems, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • R.S. Judson, M.E. Colvine, J.C. Meza, A. Huffer and D. Gutierrez, Do intelligent configuration search techniques outperform random search for large molecules, International Journal of Quantum Chemistry 44 (1992) 277.

    Article  Google Scholar 

  • K. Juliff, A multi-chromosome genetic algorithm for pallet loading, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 467.

    Google Scholar 

  • R.A. Julstrom, A genetic algorithm for the rectilinear steiner problem, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 474.

    Google Scholar 

  • N. Kadaba, XROUTE. A knowledge-based routing system using neural networks and genetic algorithms, Ph.D. Dissertation, North Dakota State University (1990).

  • R. Kalaba, M. Kim and J.E. Moore, Linear programming and recurrent associative memories, International Journal of General Systems 20 (1992) 177.

    Google Scholar 

  • J.H. Kalivas, Optimization using variations of simulated annealing, Chemometrics and Intelligent Laboratory Systems 15 (1992) 1.

    Article  Google Scholar 

  • T. Kampke, Simulated annealing: Use of a new tool in bin packing, Annals of Operations Research 16 (1988) 327.

    Google Scholar 

  • S. Kaparthi and N.C. Suresh, Performance of selected part-machine grouping techniques for data sets of wide ranging sizes and imperfection, Decision Sciences 25 (1994) 515.

    Google Scholar 

  • S. Kaparthi, N.C. Suresh and R.P. Cerveny, An improved neural-network leader algorithm for part-machine grouping in group technology, European Journal of Operational Research 69 (1993) 342.

    Article  Google Scholar 

  • A. Kapsalis, P. Chardaire, V.J. Rayward-Smith and G.D. Smith, The radio link frequency assignment problem. A case study using genetic algorithms,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • A. Kapsalis, V.J. Rayward-Smith and G.D. Smith, Solving the graphical Steiner tree problem using genetics, Journal of the Operational Research Society 44 (1993) 397.

    Google Scholar 

  • V. Karamcheti and M. Malek, A TSP engine for performing tabu search, in:Proceedings of the International Conference on Application Specific Array Processing (IEEE Computer Society Press, Los Alamitos, California, 1991) p. 309.

    Google Scholar 

  • T. Kawaguchi and T. Todaka, Operation scheduling by annealed neural networks, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E78A (1995) 656.

  • A.J. Keane, A brief comparison of some evolutionary optimization methods, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • A.J. Keane, Genetic algorithm optimization of multi-peak problems. Studies in convergence and robustness, Artificial Intelligence in Engineering 9 (1995) 75.

    Article  Google Scholar 

  • J.P. Kelly, B.L. Golden and A.A. Assad, Large-scale controlled rounding using tabu search with strategic oscillation, Annals of Operations Research 41 (1993) 69.

    Article  Google Scholar 

  • J.P. Kelly, M. Laguna and F. Glover, A study of diversification strategies for the quadratic assignment problem, Computers and Operations Research 21 (1994) 885.

    Article  Google Scholar 

  • W. Kern, On the depth of combinatorial optimization problems, Discrete Applied Mathematics 43 (1993) 115.

    Article  Google Scholar 

  • T. Kido, K. Takagi and M. Nalanishi, Analysis and comparisons of genetic algorithm, simulated annealing, tabu search and evolutionary combination algorithm, Informatica 18 (1994) 399.

    Google Scholar 

  • T. Kido, H. Kitano and M. Nakamishi, A hybrid search for genetic algorithms. Combining genetic algorithms, tabu search and simulated annealing, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 641.

    Google Scholar 

  • M.D. Kidwell, Using genetic algorithms to schedule distributed tasks on a bus-based system, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 368.

    Google Scholar 

  • C. Kim, W. Kim, H. Shin, K. Rhee, H. Chung and J. Kim, Combined hierarchical placement algorithm for row-based layouts, Electronics Letters 29 (1993) 1508.

    Google Scholar 

  • H. Kim, K. Nara and M. Gen, A method for maintenance scheduling using GA combined with SA, Computers and Industrial Engineering 27 (1994) 477.

    Article  Google Scholar 

  • Y.T. Kim, Y.J. Jang and M.W. Kim, Stepwise overlapped parallel annealing and its application to floorplan designs, Computer-Aided Design 23 (1991) 133.

    Article  Google Scholar 

  • R.K. Kincaid, Solving the damper placement problem via local search heuristics, OR Spektrum 17 (1995) 149.

    Article  Google Scholar 

  • R.K. Kincaid, Minimizing distortion in truss structures. A comparison of simulated annealing and tabu search, Structural Optimization 5 (1993) 217.

    Article  Google Scholar 

  • R.K. Kincaid, Good solutions to discrete noxious location problems via metaheuristics, Annals of Operations Research 40 (1992) 265.

    Article  Google Scholar 

  • R.K. Kincaid, A.D. Martin and J.A. Hinkley, Heuristic search for the polymer straightening problem, Computational Polymer Science 5 (1995) 1.

    Google Scholar 

  • W. Kinnebrock, Accelerating the standard backpropagation method using a genetic approach, Neurocomputing 6 (1994) 583.

    Article  Google Scholar 

  • S. Kirkpatrick, C.D. Gelatt and P.M. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671.

    Google Scholar 

  • L.M. Kirousis, Fast parallel constraint satisfaction, Artificial Intelligence 64 (1993) 147.

    Article  Google Scholar 

  • B.S. Kiselev, N.Y. Kulakov and A.L. Mikaelyan, A modification of the annealing imitation method for solving combinatorial optimization problems, Telecommunications and Radio Engineering 48 (1993) 123.

    Google Scholar 

  • H. Kita, H. Odani and I. Nishikawa, Solving a placement problem by means of an analog neural network, IEEE Transactions on Industrial Electronics 39 (1992) 543.

    Article  Google Scholar 

  • H. Kitano, Neurogenetic learning. An integrated method of designing and training neural networks using genetic algorithms, Physica D75 (1994) 225.

    Google Scholar 

  • R.W. Klein and K.C. Dubes, Experiments in projection and clustering by simulated annealing, Pattern Recognition 22 (1989) 213.

    Article  Google Scholar 

  • J.G. Klincewicz, Avoiding local optima in thep-hub location problem using tabu search and GRASP, Annals of Operations Research 40 (1992) 283.

    Article  MathSciNet  Google Scholar 

  • J.G. Klincewicz and A. Rajan, Using GRASP to solve the component grouping, Working paper, AT&T Bell Laboratories, Holmdel, New Jersey (1992).

    Google Scholar 

  • J. Knox, Tabu search performance on the symmetrical traveling salesman problem, Computers and Operations Research 21 (1994) 867.

    Article  Google Scholar 

  • J. Knox, The application of tabu search to the symmetric traveling, Ph.D. Dissertation, Graduate School of Business, University of Colorado, Boulder (1989).

    Google Scholar 

  • S. Koakutsu, Y. Sugai and H. Hirata, Block placement by improved simulated annealing based on genetic algorithm,Lecture Notes in Control and Information Sciences 180 (1992) p. 648.

    Google Scholar 

  • S.J. Koh and C.Y. Lee, A tabu search for the survivable fiber optic communication-network design, Computers and Industrial Engineering 28 (1995) 689.

    Article  Google Scholar 

  • T. Kohonen,Self-Organizing and Associative Memory, 3rd edition (Springer, Berlin, 1992).

    Google Scholar 

  • T. Kohonen, Self-organized formation of topological correct feature maps, Biological Cybernetics 43 (1982) 59.

    Article  Google Scholar 

  • F. Kolahan, M. Liang and M. Zuo, Solving the combined part sequencing and tool replacement problem for an automated machining center. A tabu search approach, Computers and Industrial Engineering 28 (1995) 731.

    Article  Google Scholar 

  • A. Kolen and E. Pesch, Genetic local search in combinatorial optimization, Discrete Applied Mathematics 48 (1994) 273.

    Article  MathSciNet  Google Scholar 

  • M. Kolonko, A piecewise Markovian model for simulated annealing with stochastic cooling schedules, Journal of Applied Probability 32 (1995) 649.

    Google Scholar 

  • G. Kontoravdis and J.F. Bard, Improved heuristics for the vehicle routing problem with time windows, ORSA Journal on Computing 7 (1995) 10.

    Google Scholar 

  • H. Kopfer, Concepts of genetic algorithms and their application to the freight optimization problem in commercial shipping, OR Spektrum 14 (1992) 137.

    Article  Google Scholar 

  • H. Kopfer, G. Pankratz and E. Erkens, Development of a hybrid genetic algorithm for vehicle-routing, OR Spektrum 16 (1994) 21.

    Article  Google Scholar 

  • E. Korutcheva, M. Opper and B. Lopez, Statistical mechanics of the knapsack problem, Journal of Physics A — Mathematical and General 27 (1994) 645.

    Article  Google Scholar 

  • C. Koulmas, S.R. Antony and R. Jaen, A survey of simulated annealing applications to operations research problems, Omega 22 (1994) 41.

    Article  Google Scholar 

  • V.K. Koumousis and P.G. Georgiou, Genetic algorithms in discrete optimization of steel truss roofs, Journal of Computing in Civil Engineering 8 (1994) 309.

    Google Scholar 

  • P. Kouvelis and W.C. Chiang, A simulated annealing procedure for single row layout problems in flexible manufacturing systems, International Journal of Production Research 30 (1992) 717.

    Google Scholar 

  • P. Kouvelis, W.C. Chiang and J. Fitzsimmons, Simulated annealing for machine layout problems in the presence of zoning constraints, European Journal of Operational Research 57 (1992) 203.

    Article  Google Scholar 

  • P. Kouvelis, G.J. Gutierrez and W.C. Chiang, Heuristic unidirectional flowpath design approaches for automated guided vehicle systems, International Journal of Production Research 30 (1992) 1327.

    Google Scholar 

  • M. Kovacic, Timetable construction with markovian neural network, European Journal of Operational Research 69 (1993) 92.

    Article  Google Scholar 

  • J.R. Koza,Genetic Programming. On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA 1992).

    Google Scholar 

  • J.R. Koza,Genetic Programming II. Automatic Discovery of Reusable Subprograms (MIT Press, Cambridge, MA 1994).

    Google Scholar 

  • S.A. Kravitz and R.A. Rutenbar, Placement by simulated annealing on a multiprocessor, IEEE Transaction on Computer Aided Design 6 (1987) 534.

    Article  Google Scholar 

  • V. Kreinovich, C. Quintana and O. Fuentes, Genetic algorithms. What fitness scaling is optimal, Cybernetics and Systems 24 (1993) 9.

    Google Scholar 

  • K. Krishna, K. Ganeshan and D.J. Ram, Distributed simulated annealing algorithms for job shop scheduling, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 1102.

    Google Scholar 

  • B. Kroger, Guillotineable bin packing. A genetic approach, European Journal of Operational Research 84 (1995) 645.

    Article  Google Scholar 

  • B. Kroger, P. Schwenderling and O. Vornberger, Parallel genetic packing of rectangles,Lecture Notes in Computer Science 496 (1991) p. 160.

    Google Scholar 

  • L. Kryzanowski, M. Galler and D.W. Wright, Using artificial neural networks to pick stocks, Financial Analysts Journal 49 (1993) 21.

    Google Scholar 

  • H.M. Ku and I.M. Karimi, An evaluation of simulated annealing for batch process scheduling, Industrial and Engineering Chemistry Research 30 (1991) 163.

    Article  Google Scholar 

  • R. Kuik, M. Salomon, L.N. van Wassenhove and J. Maes, Linear programming, simulated annealing and tabu search heuristics for lotsizing in bottleneck assembly systems, IIE Transactions 25 (1993) 62.

    Google Scholar 

  • R. Kuik and M. Salomon, Multilevel lot-sizing problem. Evaluation of a simulated annealing heuristic, European Journal of Operational Research 45 (1990) 25.

    Article  MathSciNet  Google Scholar 

  • U.R. Kulkarni and M.Y. Kiang, Dynamic grouping of parts in flexible manufacturing systems. A self-organizing neural networks approach, European Journal of Operational Research 84 (1995) 192.

    Article  Google Scholar 

  • V. Kumar, Algorithms for constraint satisfaction problems. A survey, AI Magazine 13 (1992) 32.

    Google Scholar 

  • M. Kuroda and A. Kawada, Improvement on the computational efficiency of inverse queueing network analysis, Computers and Industrial Engineering 27 (1994) 421.

    Article  Google Scholar 

  • T. Kurokawa and S. Kozuka, Use of neural networks for the optimum frequency assignment problem, Electronics and Communications in Japan Part I — Communication 77 (1994) 106.

    Google Scholar 

  • V. Kvasnicka and J. Pospichal, Messay simulated annealing, Journal of Chemometrics 9 (1995) 309.

    Article  Google Scholar 

  • V. Kvasnicka and J. Pospichal, Fast evaluation of chemical distance by tabu search algorithm, Journal of Chemical Information and Computer Sciences 34 (1994) 1109.

    Article  Google Scholar 

  • M. Laguna, Clustering for the design of sonet rings in interoffice telecommunications, Management Science 40 (1994) 1533.

    Google Scholar 

  • M. Laguna, Tabu search primer, Working paper, Graduate School of Business, University of Colorado, Boulder (1993).

    Google Scholar 

  • M. Laguna, J.W. Barnes and F. Glover, Intelligent scheduling with tabu search. An application to jobs with linear delay penalties and sequence dependent setup costs and times, Applied Intelligence 3 (1993) 159.

    Article  Google Scholar 

  • M. Laguna, J.W. Barnes and F. Glover, Tabu search methods for a single-machine scheduling problem, Journal of Intelligent Manufacturing 2 (1991) 63.

    Article  Google Scholar 

  • M. Laguna and F. Glover, Bandwidth packing. A tabu search approach, Management Science 39 (1993) 492.

    Google Scholar 

  • M. Laguna and F. Glover, Integrating target analysis and tabu search for improved scheduling systems, Expert Systems with Applications 6 (1993a) 287.

    Article  Google Scholar 

  • M. Laguna, J.P. Kelly, J.L.G. Velarde and F. Glover, Tabu search for the multilevel generalized assignment problem, European Journal of Operational Research 82 (1995) 176.

    Article  Google Scholar 

  • M. Laguna and P. Laguna, Applying tabu search to the 2-dimensional Ising spin-glass, International Journal of Modern Physics C — Physics and Computers 6 (1995) 11.

    Article  Google Scholar 

  • M. Laguna and J.L.G. Velarde, A search heuristic for just-in-time scheduling in parallel machines, Journal of Intelligent Manufacturing 2 (1991) 253.

    Article  Google Scholar 

  • G. Laporte and I.H. Osman,Metaheuristics in Combinatorial Optimization, Annals of Operations Research 63 (Baltzer, Basel, 1996).

    Google Scholar 

  • G. Laporte and I.H. Osman, Routing problems. A biliography, Annals of Operations Research 61 (1995) 227.

    Article  Google Scholar 

  • S. Lash, Genetic algorithms for weighted tardiness scheduling on parallel machines, Working paper 93-01, Department of Industrial Engineering, and Management Sciences, Northwestern University, Evanston, Illinois (1993).

    Google Scholar 

  • J.B. Lasserre, P.P. Varaiya and J. Walrand, Simulated annealing, random search, multistart or sad, Systems and Control Letters 8 (1987) 297.

    Article  Google Scholar 

  • C. Lau,Neural Networks. Theoretical Foundations and Analysis (IEEE Computer Society Press, Los Alamitos, California, 1992).

    Google Scholar 

  • P.S. Laursen, An experimental comparison of 3 heuristics for the WVCP, European Journal of Operational Research 73 (1994) 181.

    Article  Google Scholar 

  • P.S. Laursen, Simulated annealing for the QAP. Optimal tradeoff between simulation time and solution quality, European Journal of Operational Research 69 (1993) 238.

    Article  Google Scholar 

  • E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan and D.B. Shmoys,The Traveling Salesman Problem. A Guided Tour of Combinatorial Optimization (Wiley, Chichester, 1985).

    Google Scholar 

  • R. Leardi, R. Boggia and M. Terrile, Genetic algorithms as a strategy for feature-selection, Journal of Chemometrics 6 (1992) 267.

    Article  Google Scholar 

  • J.P. Leclercq and V. Englebert, Automatic graph's drawing, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 33.

    Google Scholar 

  • B.W. Lee and B.J. Sheu, Paralleled hardware annealing for optimal solutions on electronic neural networks, IEEE Transactions on Neural Networks 4 (1993) 588.

    Article  Google Scholar 

  • B.W. Lee and B.J. Sheu,Hardware Annealing in Analog VLSI Neurocomputing (Kluwer, Boston, 1991).

    Google Scholar 

  • C.K. Lee and K.I. Yang, Network design of one-way streets with simulated annealing, Papers in Regional Science 73 (1994) 119.

    Google Scholar 

  • C.Y. Lee, Genetic algorithms for single-machine job scheduling with common due-date and symmetrical penalties, Journal of the Operations Research Society of Japan 37 (1994) 83.

    Google Scholar 

  • C.Y. Lee and J.Y. Choi, A genetic algorithm for job sequencing problems with distinct due-dates and general early-tardy penalty weights, Computers and Operations Research 22 (1995) 857.

    Article  Google Scholar 

  • C.Y. Lee and S.J. Kim, Parallel genetic algorithms for the earliness tardiness job scheduling problem with general penalty weights, Computers and Industrial Engineering 28 (1995) 231.

    Article  Google Scholar 

  • F.H. Lee, G.S. Stiles and V. Swaminathan, Parallel annealing on distributed memory systems, Programming and Computer Software 21 (1995) 1.

    Google Scholar 

  • I. Lee, R. Sikora and M.J. Shaw, Joint lot sizing and sequencing with genetic algorithms for scheduling and evolving the chromosome structure, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 383.

    Google Scholar 

  • J. Lee, J.H. Chou and S.L. Fu, New approach for the ordering of gate permutation in one-dimensional logic-arrays, IEE Proceedings — Circuits Devices and Systems 142 (1995) 90.

    Article  Google Scholar 

  • J.Y. Lee and M.Y. Choi, Optimization by multicanonical annealing and the traveling salesman problem, Physical Review E50 (1994) 651.

    Google Scholar 

  • S. Lee and H.P. Wang, Modified simulated annealing for multipleobjective engineering design optimization, Journal of Intelligent Manufacturing 3 (1992) 101.

    Article  Google Scholar 

  • Y.N. Lee, G.P. McKeown and V.J. Rayward-Smith, The convoy movement problem with initial delays, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • V.J. Leon and R. Balakrishnan, Strength and adaptibility of problem-space based neighborhoods for resource constrained scheduling, OR Spektrum 17 (1995) 173.

    Article  Google Scholar 

  • C. LePape, Constraint-based programming for scheduling. An historical perspective, Working paper, ILOG, Gentilly, France (1994).

    Google Scholar 

  • C. LePape, Implementation of resource constraints in ILOG schedule. A library for the development of constraint based scheduling systems, Intelligent Systems Engineering 3 (1994) 55.

    Google Scholar 

  • Y.Y. Leu, L.A. Matheson and L.P. Rees, Assembly-line balancing using genetic algorithms with heuristic-generated initial populations and multiple evaluation criteria, Decision Sciences 25 (1994) 581.

    Google Scholar 

  • J. Lever, M. Wallace and B. Richards, Constraint logic programming for scheduling and planning, BT Technology Journal 13 (1995) 73.

    Google Scholar 

  • D.M. Levine, A parallel genetic algorithm for the set partitioning problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • D.M. Levine, PGAPack V0.2: A Data-Structure Neutral Parallel Genetic Algorithm Library, University of Chicago, Argonne National Laboratory, Illinois (1995) (ftp site address: info.mcs.anl.gov,files in: /pub/pgpack/pgpack.tar.Z).

    Google Scholar 

  • D.M. Levine, A parallel genetic algorithm for the set partitioning problem, Ph.D. Dissertation, Department of Computer Science, Illinois Institute of Technology, Chicago (1994).

    Google Scholar 

  • D.M. Levine, A genetic algorithm for the set partitioning problem, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 481.

    Google Scholar 

  • J.H. Li and S. Nishihara, A constraint satisfaction algorithm using solution trees and its complexity, IFIP Transactions A — Computer Science and Technology 19 (1992) 229.

    Google Scholar 

  • Y. Li, P.M. Pardalos and M.G.C. Resende, A greedy randomized adaptive search procedure for the quadratic assignment problem, in:Quadratic Assignment and Related Problems, ed. P.M. Pardalos and H. Wolkowicz,DIMACS Series on Discrete Mathematics and Theoretical Computer Science 16 (1994).

  • Y.H. Li and Y.J. Jiang, Localized simulated annealing in constraint satisfaction and optimization, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • G.E. Liepins and S. Baluja, APGA. An adaptive parallel genetic algorithm, in:Computer Science and Operations Research New Development in their Interfaces, ed. O. Balci, R. Sharda and S.A. Zenios (Pergamon Press, Oxford, 1992).

    Google Scholar 

  • G.E. Liepins, M. Hilliard, J. Richardson and M. Palmer, Genetic algorithm applications to set covering and traveling salesman problems, in:Operations Research and Artificial Intelligence. The Integrations of Problem Solving Strategies, ed. E.D. Brown and White (Kluwer, Boston, 1990).

    Google Scholar 

  • G.E. Liepins G.E. Hilliard and M. Hilliard, Genetic algorithms. Foundations and applications, Annals of Operations Research 21 (1989) 31.

    Article  Google Scholar 

  • G.E. Liepins and M.D. Vose, Characterizing crossover in genetic algorithms, Annals of Mathematics and Artificial Intelligence 5 (1991) 27.

    Article  Google Scholar 

  • G.E. Liepins and M.D. Vose, Representational issues in genetic optimization, Journal of Experimental and Theoretical Artificial Intelligence 2 (1990) 101.

    Google Scholar 

  • W.E. Lillo, M.H. Lob, S. Hui and S.H. Zak, On solving constrained optimization problems with neural networks. A penalty method approach, IEEE Transactions on Neural Networks 4 (1993a) 931.

    Article  Google Scholar 

  • W.E. Lillo, S. Hui and S.H. Zak, Neural networks for constrained optimization problems, International Journal of Circuit Theory and Applications 21 (1993b) 385.

    Google Scholar 

  • C.K.Y. Lin, K.B. Haley and C. Sparks, A comparative study of both standard and adaptive versions of threshold accepting and simulated annealing algorithms in 3 scheduling problems, European Journal of Operational Research 83 (1995) 330.

    Article  Google Scholar 

  • C.T. Lin and C.S.G. Lee, A multivalued Boltzmann machine, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 660.

    Google Scholar 

  • F.T. Lin, C.Y. Kao and C.C. Hsu, Applying the genetic approach to simulated annealing in solving some NP-hard problems, IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 1752.

    Google Scholar 

  • F.T. Lin, C.Y. Kao and C.C. Hsu, Incorporating genetic algorithms into simulated annealing, in:Proceedings of the 4th International Symposium on Artificial Intelligence (1991) p. 290.

  • S.C. Lin and J.H.C. Hsueh, A new methodology of simulated annealing for the optimization problems, Physica A205 (1994a) 367.

    Google Scholar 

  • S.C. Lin and J.H.C. Hsueh, Nearest neighbor heuristics in accelerated algorithms of optimization problems, Physica A203 (1994b) 369.

    Google Scholar 

  • R.P. Lippmann, An introduction to computing with neural nets, IEEE ASSP Magazine 4 (1987) 4.

    Google Scholar 

  • Y. Lirov, Computer-aided neural network engineering, Neural Networks 5 (1992a) 711.

    Google Scholar 

  • Y. Lirov, Knowledge based approach to the cutting stock problem, Mathematical and Computer Modelling 16 (1992b) 107.

    Article  Google Scholar 

  • P.G.J. Lisboa,Neural Networks: Current Applications (Chapman and Hall, London, 1992).

    Google Scholar 

  • R. Lister, Multiprocessor rejectionless annealing, in:Proceedings of Parallel Computing and Transputers, PCAT'93, ed. D. Arnold, R. Christer, D. Day and P. Roe (ISO Press, Amsterdam, 1994) p. 199.

    Google Scholar 

  • J. Little and K. Darby-Dowman, The significance of constraint logic programming to operational research, in:Operational Research Tutorial Papers, ed. M. Lawrence and C. Wilsdon (Operational Research Society, Birmingham, 1995).

    Google Scholar 

  • C.M. Liu, R.L. Kao and A.H. Wang, Solving location-allocation problems with rectilinear distances by stimulated annealing, Journal of the Operational Research Society 45 (1994) 1304.

    Google Scholar 

  • C.M. Liu and J.K. Wu, Machine cell-formation using the simulated annealing algorithm, International Journal of Computer Integrated Manufacturing 6 (1993) 335.

    Google Scholar 

  • X.Z. Liu, A. Sakamoto and T. Shimamoto, Genetic channel router, IEIECE Transactions on Fundamentals of Electronics Communications and Computer Sciences E77A (1994) 492.

  • X.Z. Liu, A Sakamoto and T. Shimamoto, Restrictive channel routing with evolution programs, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E76A (1993) 1738.

  • Z.P. Lo and B. Bavarian, Multiple job scheduling with artificial neural networks, Computers and Electrical Engineering 19 (1993) 87.

    Article  Google Scholar 

  • Z.P. Lo and B. Bavarian, Optimization of job scheduling on parallel machines by simulated annealing algorithms, Expert Systems with Applications 4 (1992) 323.

    Article  Google Scholar 

  • C. Lockwood and T. Moore, Harvest scheduling with spatial constraints. A simulated annealing approach, Canadian Journal of Forest Research — Journal Canadien de la Recherche Forestière 23 (1993) 468.

    Google Scholar 

  • R. Logendran and P. Ramakrishna, Manufacturing cell formation in the presence of lot splitting and multiple units of the same machine, International Journal of Production Research 33 (1995) 675.

    Google Scholar 

  • R. Logendran, P. Ramakrishna and C. Sriskandarajah, Tabu search based heuristics for cellular manufacturing systems in the presence of alternative process plans, International Journal of Production Research 32 (1994) 273.

    Google Scholar 

  • A. Løkketangen, Tabu search. Using the search experience to guide the search process. An introduction with examples, AI Communications 8 (1995) 78.

    Google Scholar 

  • A. Løkketangen and F. Glover, Probabilistic move selection in tabu search for zero-one mixed integer programming problems, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • A. Løkketangen and F. Glover, Tabu search for zero-one mixed integer programming problems with advanced level strategies and learning, International Journal of Operations and Quantitative Management 1 (1995), forthcoming.

  • A. Løkketangen, K. Jörnsten and S. Storoy, Tabu search with a pivot and complement framework, International Transactions in Operational Research 1 (1994) 305.

    Article  Google Scholar 

  • C.K. Looi, Neural network methods in combinatorial optimization, Computers and Operations Research 19 (1992) 191.

    Article  Google Scholar 

  • H.R. Lourenço, Job shop scheduling. Computational study of local search and large-step optimization methods, European Journal of Operational Research 83 (1995) 347.

    Article  Google Scholar 

  • H.R. Lourenço and M. Zwijnenburg, Combining the large-step optimization with tabu-search. Application to the job shop scheduling problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • C.B. Lucasius and G. Kateman, Understanding and using genetic algorithms 1. Concepts, properties and context, Chemometrics and Intelligent Laboratory Systems 19 (1993) 1.

    Article  Google Scholar 

  • C.B. Lucasius and G. Kateman, Understanding and using genetic algorithms 2. Representation, configuration and hybridization, Chemometrics and Intelligent Laboratory Systems 25 (1994a) 99.

    Article  Google Scholar 

  • C.B. Lucasius and G. Kateman, Gates towards evolutionary large-scale optimization. A software-oriented approach to genetic algorithms 1. General perspective, Computers and Chemistry 18 (1994b) 127.

    Article  Google Scholar 

  • C.B. Lucasius and G. Kateman, Gates towards evolutionary large-scale optimization. A software-oriented approach to genetic algorithms 2. Toolbox description, Computers and Chemistry 18 (1994c) 137.

    Article  Google Scholar 

  • S. Lundy and A. Mees, Convergence of an annealing algorithm, Mathematical Programming 34 (1986) 111.

    Article  Google Scholar 

  • H. Lutfiyya, B. McMillin, P. Poshyanonda and C. Dagli, Composite stock cutting through simulated annealing, Mathematical and Computer Modelling 16 (1992) 57.

    Article  Google Scholar 

  • J.-L. Lutton and E. Philippart, A simulated annealing algorithm for the computation of marginal costs of telecommunication links, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • J.-L. Lutton and E. Bonomi, Simulated annealing algorithm for the minimum weighted perfect Euclidean matching problem, RAIRO — Operations Research 20 (1986) 177.

    Google Scholar 

  • A.K. Mackworth and E.C. Freuder, The complexity of constraint satisfaction revisited, Artifical Intelligence 59 (1993) 57.

    Article  Google Scholar 

  • A.K. Mackworth, Constraint satisfaction, in:Encyclopedia of Artificial Intelligence, Vol. 1, ed. S.C. Shaprio (Wiley, Chichester, 1992a).

    Google Scholar 

  • A.K. Mackworth, The logic of constraint satisfaction, Artificial Intelligence 58 (1992b) 3.

    Article  MathSciNet  Google Scholar 

  • S.W. Mahfoud, Finite Markov chain models of an alternative selection strategy for the genetic algorithm, Complex Systems 7 (1993) 493.

    Google Scholar 

  • S.W. Mahfoud and D.E. Goldberg, Parallel recombinative simulated annealing. A genetic algorithm, Parallel Computing 21 (1995) 1.

    Article  MathSciNet  Google Scholar 

  • D. Maio, D. Maltoni and S. Rizzi, Topological clustering of maps using a genetic algorithm, Pattern Recognition Letters 16 (1995) 89.

    Article  Google Scholar 

  • A.K. Majhi, L.M. Patnaik and S. Raman, A genetic algorithm-based circuit partitioner for MCMS, Microprocessing and Microprogramming 41 (1995) 83.

    Article  Google Scholar 

  • E. Makinen and M. Sieranta, Genetic algorithms for drawing bipartite graphs, International Journal of Computer Mathematics 53 (1994) 157.

    Google Scholar 

  • B. Malakooti, J. Wang and E.C. Tandler, A sensor-based accelerated approach for multi-attribute machinability and tool life evaluation, International Journal of Production Research 28 (1990) 2373.

    Google Scholar 

  • B. Malakooti and Y.Q. Zhou, Feedforward artificial neural networks for solving discrete multiple criteria decision making problems, Management Science 40 (1994) 1542.

    Google Scholar 

  • M. Malek, M. Guruswamy, M. Pandya and H. Owens, Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem, Annals of Operations Research 21 (1989) 59.

    Article  Google Scholar 

  • C.J. Malmborg, Optimization of cube-per-order index warehouse layouts with zoning constraints, International Journal of Production Research 33 (1995) 465.

    Google Scholar 

  • C.J. Malmborg, Heuristic, storage space minimisation methods for facility layouts served by looped AGV systems, International Journal of Production Research 32 (1994) 2695.

    Google Scholar 

  • R.J. Mammone and Y.Y. Zeevi,Neural Networks: Theory and Applications (Academic Press, London, 1991).

    Google Scholar 

  • J. Mandziuk, Solving then-queens problem with a binary Hopfield type network. Synchronous and asynchronous model, Biological Cybernetics 72 (1995) 439.

    Article  Google Scholar 

  • V. Maniezzo, Genetic evolution of the topology and weight distribution of neural networks, IEEE Transactions on Neural Networks 5 (1994) 39.

    Article  Google Scholar 

  • V. Maniezzo, M. Dorigo and A. Colorni, ALGODESK. An experimental comparison of eight evolutionary heuristics applied to the quadratic assignment problem, European Journal of Operational Research 81 (1995) 188.

    Article  Google Scholar 

  • J.W. Mann, A. Kapsalis and G.D. Smith, The GAmeter toolkit, in:Applications of Modern Heuristic Methods, ed. V.J. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995).

    Google Scholar 

  • J.W. Mann and G.D. Smith, A comparison of heuristics for telecommunications traffic routing, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • N. Mansour, Parallel physical optimization algorithms for allocating data to multicomputer nodes, Journal of Supercomputing 8 (1994) 53.

    Article  Google Scholar 

  • N. Mansour and G.C. Fox, Allocating data to distributed memory multiprocessors by genetic algorithms, Concurrency — Practice and Experience 6 (1994) 485.

    Google Scholar 

  • N. Mansour and G.C. Fox, Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations, Concurrency — Practice and Experience 4 (1992a) 557.

    Google Scholar 

  • N. Mansour and G.C. Fox, Parallel physical optimization algorithms for data mapping,Lecture Notes in Computer Science 634 (1992b) p. 91.

    Google Scholar 

  • R. Marett and M. Wright, A comparison of neighbourhood search techniques for multi-objective combinatorial problems, Working paper, Department of Management Science, University of Lancaster, England (1994).

    Google Scholar 

  • E. Marinari and G. Parisi, Simulated tempering. A new Monte Carlo scheme, Europhysics Letters 19 (1992) 451.

    Google Scholar 

  • S.J. Marshall and R.F. Harrison, Optimization and training of feed forward neural networks by genetic algorithms, in:Proceedings of the 2nd IEEE International Conference on Artificial Neural Networks (1991) p. 39.

  • R. Martí, An aggressive search procedure for the bipartite drawing problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • O.C. Martin and S.W. Otto, Combining simulated annealing with local search heuristics, Annals of Operations Research 63 (1996) 57.

    Google Scholar 

  • O.C. Martin and S.W. Otto, Partitioning of unstructured meshes for load balancing, Concurrency — Practice and Experience 7 (1995) 303.

    Google Scholar 

  • O.C. Martin, S.W. Otto and E.W. Felten, Large-step Markov chains for the TSP, incorporating local search heuristics, Operations Research Letters 11 (1992) 219.

    Article  MathSciNet  Google Scholar 

  • O.C. Martin, S.W. Otto and E.W. Felten, Large-step Markov chains for the TSP, Complex Systems 5 (1991) 299.

    MathSciNet  Google Scholar 

  • F. Maruyama, Y. Minoda and S. Sawada, A logical framework for constraint programming, Fujitsu Scientific and Technical Journal 30 (1994) 69.

    Google Scholar 

  • R. Mason, R. Gunst and J. Hess,Statistical Design and Analysis of Experiments (Wiley, Chichester, 1989).

    Google Scholar 

  • R. Mathar and A. Zilinskas, On global optimization in 2-dimensional scaling, Acta Applicandae Mathematicae 33 (1993) 109.

    Article  MathSciNet  Google Scholar 

  • R. Mathar and J. Mattfeldt, Channel assignment in cellular radio networks, IEEE Transactions on Vehicular Technology 42 (1993) 647.

    Article  Google Scholar 

  • R. Mathieu, L. Pittard and G. Anandalingam, Genetic algorithm based approach to bi-level linear programming, RAIRO — Operations Research 28 (1994) 1.

    Google Scholar 

  • Y. Matsuyama, Self-organization neural networks and various Euclidean travelling salesman problems, Systems and Computers in Japan 23 (1992) 101.

    Google Scholar 

  • Y. Matsuyama, Self-organization via competition, co-operation and categorization applied to extended vehicle routing problems, in:Proceedings of the International Joint Conference on Neural Networks, Seattle, WA (1991) p. I-385.

  • M. Matysiak, Efficient optimization of large join queries using tabu search, Information Sciences 83 (1995) 77.

    Article  Google Scholar 

  • T. Mavridou, P.M. Pardalos, L.S. Pitsoulis and M.G.C. Resende, A GRASP for the biquadratic assignment problem, Working paper, AT&T Bell Laboratories, Murray Hill, New Jersey (1995).

    Google Scholar 

  • E. Mayrand, P. Lefrançois, O. Kettani and M.-H. Jobin, A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility, OR Spektrum 17 (1995) 183.

    Article  Google Scholar 

  • C. Mazza, Parallel simulated annealing, Random Structures and Algorithms 3 (1992) 139.

    Google Scholar 

  • S.I. McClean, D.A. Bell and F.J. McErlean, Heuristic methods for the data placement problem, Journal of the Operational Research Society 42 (1991) 767.

    Google Scholar 

  • S.I. McClean, D.A. Bell and F.J. McErlean, The use of simulated annealing for clustering data in databases, Information Systems 15 (1990) 233.

    Article  Google Scholar 

  • C.C. McGeoch, Experimental analysis of algorithms, Ph.D. Dissertation, CMU-CS-87-124, Computer Science Department, Carnegie Mellon University (1986).

  • I.I. Melamed, Neural networks and combinatorial optimization, Automation and Remote Control 55 (1994) 1553.

    Google Scholar 

  • J.B.M. Melissen and P.C. Schuur, Packing 16, 17 or 18 circles in an equilateral triangle, Discrete Mathematics 145 (1995) 333.

    Article  Google Scholar 

  • F. Menczer and D. Parisi, Evidence of hyperplanes in the genetic learning of neural networks, Biological Cybernetics 66 (1992a) 283.

    Article  PubMed  Google Scholar 

  • F. Menczer and D. Parisi, Recombination and unsupervised learning. Effects of crossover in the genetic optimization of neural networks, Network — Computation in Neural Systems 3 (1992b) 423.

    Article  Google Scholar 

  • F. Menezes and P. Barahona, Heuristics and look-ahead integration to solve constraint satisfaction problems efficiently, Annals of Operations Research 50 (1994) 411.

    Article  Google Scholar 

  • W. Mergenthaler, W. Stadler, H. Wilbertz and N. Zimmer, Optimizing automotive manufacturing sequences using simulated annealing and genetic algorithms, Control Engineering Practice 3 (1995) 569.

    Article  Google Scholar 

  • P. Meseguer, Constraint satisfaction problems. An overview, AI Communications 2 (1989) 3.

    Google Scholar 

  • Z. Michalewicz, Evolutionary computation and heuristics, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston 1996).

    Google Scholar 

  • Z. Michalewicz, Evolutionary computation techniques for non-linear programming problems, International Transactions in Operational Research 1 (1994a) 223.

    Article  Google Scholar 

  • Z. Michalewicz, Nonstandard methods in evolutionary computation, Statistics and Computing 4 (1994b) 141.

    Google Scholar 

  • Z. Michalewicz, A hierarchy of evolution programs. An experimental study, Evolutionary Computation 1 (1993) 51.

    Google Scholar 

  • Z. Michalewicz,Genetic Algorithms + Data Structures = Evolution Programs (Springer, Berlin, 1992).

    Google Scholar 

  • Z. Michalewicz, G.A. Vignaux and M. Hobbs, A non-standard genetic algorithm for non-linear transportation problem, ORSA Journal on Computing 3 (1991) 307.

    Google Scholar 

  • Z. Michalewicz and C. Janikow, GENOCOP. A genetic algorithm for numerical optimization problems with linear constraints, Communications of the ACM (1995), forthcoming.

  • Z. Michalewicz and C. Janikow, Genetic algorithms for numerical optimization, Statistics and Computing 1 (1993) 75.

    Article  Google Scholar 

  • Z. Michalewicz and C.Z. Janikow, Handling constraints in genetic algorithms, in:Proceedings of the 4th International Conference on Genetic Algorithms, ed. R.K. Belew and L.B. Booker (Morgan Kaufmann, San Mateo 1991) p. 151.

    Google Scholar 

  • P. Michelon, M.D. Cruz and V. Gascon, Using the tabu search for the distribution of supplies in a hospital, Annals of Operations Research 50 (1994) 427.

    Article  Google Scholar 

  • L. Miclo, Remarks on ergodicity of simulated annealing algorithms on a graph, Stochastic Processes and their Applications 58 (1995) 329.

    Article  Google Scholar 

  • G.F. Miller and P.M. Todd, The role of mate choice in biocomputation. Sexual selection as a process of search, optimization and diversification,Lecture Notes in Computer Science 899 (1995) p. 170.

    Google Scholar 

  • G.F. Miller, P.M. Todd and S.U. Hedge, Designing neural networks, Neural Networks 4 (1991) 53.

    Article  Google Scholar 

  • J.A. Miller, W.D. Potter, R.V. Gandham and C.N. Lapena, An evaluation of local improvement operators for genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 1340.

    Article  Google Scholar 

  • M. Minagawa and Y. Kakazu, A genetic approach to the line balancing problem, IFIP Transactions B — Applications in Technology 3 (1992) 737.

    Google Scholar 

  • S. Minton, M.D. Johnston, A.B. Philips and P. Laird, Minimizing conflicts. A heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence 58 (1992) 161.

    Article  MathSciNet  Google Scholar 

  • V. Miranda, J.V. Ranito and L.M. Proenca, Genetic algorithms in optimal multistage distribution network planning, IEEE Transactions on Power Systems 9 (1994) 1927.

    Article  Google Scholar 

  • G. Mirkin, K. Vasudevan, F.A. Cook, W.G. Laidlaw and W.G. Wilson, A comparison of several cooling schedules for simulated annealing implemented on a residual statics problem, Geophysical Research Letters 20 (1993) 77.

    Google Scholar 

  • M. Mitchell,An Introduction to Genetic Algorithms (The MIT Press, Cambridge, 1996).

    Google Scholar 

  • M. Mitchell, and J.H. Holland, When will a genetic algorithm outperform hill-climbing?, Working paper, Santa Fe Institute, Santa Fe, New Mexico (1993).

    Google Scholar 

  • D. Mitra, F. Romeo and A. Sangiovanni-Vincentelli, Convergence and finite-time behavior of simulated annealing, Advances in Applied Probability 18 (1986) 747.

    Google Scholar 

  • K. Mitsuo, T. Santoso, M. Mas and T. Takashi, An optimization technique with neural networks and its application to a ferry routing,Lecture Notes in Control and Information Sciences 143 (1990) p. 800.

    Google Scholar 

  • J. Mittenthal, M. Raghavachari and A.I. Rana, A hybrid simulated annealing approach for single machine scheduling problems with nonregular penalty functions, Computers and Operations Research 20 (1993) 103.

    Article  Google Scholar 

  • J.V. Moccellin, A new heuristic method for the permutation flow-shop scheduling problem, Journal of the Operational Research Society 46 (1995) 883.

    Google Scholar 

  • P. Molitor, Layer assignment by simulated annealing, Microprocessing and Microprogramming 16 (1985) 345.

    Article  Google Scholar 

  • Y.B. Moon and S.C. Chi, Generalized part family formation using neural network techniques, Journal of Manufacturing Systems 11 (1992) 149.

    Google Scholar 

  • E.L. Mooney and R.L. Rardin, Tabu search for a class of scheduling problems, Annals of Operations Research 41 (1993) 253.

    Article  Google Scholar 

  • L.B. Morales, R. Gardunojuarez and D. Romero, The multiple-minima problem in small peptides revisited. The threshold accepting approach, Journal of Biomolecular Structure and Dynamics 9 (1992) 951.

    PubMed  Google Scholar 

  • K. Morizawa, H. Nagasawa and N. Nishiyama, Complex random sample scheduling and its application to anN/M/F/F-max problem, Computers and Industrial Engineering 27 (1994) 23.

    Article  Google Scholar 

  • T. Morton and P. Ramnath, Guided forward search in tardiness scheduling of large one machine problems, in:Intelligent Scheduling Systems, ed. D.E. Brown and W.T. Scherer,Operations Research/Computer Science Interfaces 3 (Kluwer, Boston, 1995).

    Google Scholar 

  • P. Moscato, An introduction to population approaches for optimization and hierarchical objective functions. A discussion on the role of tabu search, Annals of Operations Research 41 (1993) 85.

    Article  Google Scholar 

  • P. Moscato and J.F. Fontanari, Stochastic versus deterministic update in simulated annealing, Physics Letters A146 (1990) 204.

    Google Scholar 

  • H. Mühlenbein, Evolutionary algorithms. Theory and applications, in:Local Search in Combinatorial Optimization, ed. E.H.L. Aarts and J.K. Lenstra (Wiley, Chichester 1996).

    Google Scholar 

  • H. Mühlenbein, How genetic algorithms really work I. Mutation and hillclimbing, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Maderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • H. Mühlenbein, Parallel genetic algorithms, population genetics and combinatorial optimization,Lecture Notes in Artificial Intelligence 565 (1991) p. 398.

    Google Scholar 

  • H. Mühlenbein, Parallel genetic algorithms, population genetics and combinatorial optimization in:Proceedings of the 3rd International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo 1989) p. 71.

    Google Scholar 

  • H. Mühlenbein, M. Gorges-Schleuter and O. Krämer, New solutions to the mapping problem of parallel systems. The evolution approach, Parallel Computing 4 (1987) 269.

    Article  Google Scholar 

  • H. Mühlenbein and D. Schlierkamp-Voosen, Analysis of selection, mutation and recombination in genetic algorithms,Lecture Notes in Computer Science 899 (1995) p. 142.

    Google Scholar 

  • H. Mühlenbein and D. Schlierkamp-Voosen, The science of breeding and its application to the breeder genetic algorithm, Evolutionary Computation 1 (1994) 335.

    Google Scholar 

  • H. Mühlenbein and D. Schlierkamp-Voosen, Predictive models for the breeder genetic algorithm I. Continuous parameter optimization, Evolutionary Computation 1 (1993) 25.

    Google Scholar 

  • H. Mühlenbein, M. Schomisch and J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (1991) 619.

    Article  Google Scholar 

  • H. Mühlenbein and H.-M. Voigt, Gene pool recombination in genetic algorithms, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • H. Mulkens, Revisiting the Johnson algorithm for flowshop scheduling with genetic algorithms, IFIP Transactions B — Applications in Technology 15 (1994) 69.

    Google Scholar 

  • B. Muller and J. Reinhardt,Neural Networks: An Introduction (Springer, Berlin, 1991).

    Google Scholar 

  • A.T. Murray and R.L. Church, Heuristic solution approaches to operational forest planning problems, OR Spektrum 17 (1995) 193.

    Article  Google Scholar 

  • C.V.R. Murthy and G. Srinivasan, Fractional cell-formation in group technology, International Journal of Production Research 33 (1995) 1323.

    Google Scholar 

  • K.L. Musser, J.S. Dhingra and G.L. Blankenship, Optimization based job shop scheduling, IEEE Transactions on Automatic Control 38 (1993) 808.

    Article  Google Scholar 

  • P.M. Mutalik, L.R. Knight, J.L. Blanton and R.L. Wainwright, Solving combinatorial optimization problems using parallel simulated annealing and parallel genetic algorithms, in:Proceedings of the 1991 ACM/IEE Symposium on Applied Computing (1992) p. 1031.

  • B.A. Nadel, Constraint satisfaction algorithms, Computational Intelligence 5 (1989) 188.

    Google Scholar 

  • A. Nagar, S.S. Heragu and J. Haddock, A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem, Annals of Operations Research 63 (1996) 397.

    Google Scholar 

  • R. Nakano and T. Yamada, Conventional genetic algorithms for job shop problems, in:Proceedings of the 4th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo 1991) p. 474.

    Google Scholar 

  • K. Naphade, S.D. Wu and R.H. Storer, A problem space method for the resource constraint project scheduling problem, Working paper 94T-005, Department of Industrial Engineering, Lehigh University, Bethlehem, PA (1994).

    Google Scholar 

  • M.M. Nelson and W.T. Illingworth,A Practical Guide to Neural Nets (Addison-Wesley, Wokingham, England, 1991).

    Google Scholar 

  • G.L. Nemhauser and L.A. Wolsey,Integer and Combinatorial Optimization (Wiley, Chichester, 1988).

    Google Scholar 

  • J.A. Nestor and G. Krishnamoorthy, SALSA. A new approach to scheduling with timing constraints, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 12 (1993) 1107.

    Article  Google Scholar 

  • N.K. Nguyen, An algorithm for constructing optimal resolvable incomplete block designs, Communications in Statistics — Simulation and Computation 22 (1993) 911.

    Google Scholar 

  • Y. Nishibe, K. Kuwabara, M. Yokoo and T. Ishida, Speed-up and application distributed constraint satisfaction to communication network path assignments, Systems and Computers in Japan 25 (1994) 54.

    Google Scholar 

  • V. Nissen, Solving the quadratic assignment problem with clues from nature, IEEE Transactions on Neural Networks 5 (1994) 66.

    Article  Google Scholar 

  • V. Nissen, Evolutionary algorithms in mangement science. An overiew and list of references, Report No. 9303, Universität Göttingen, Germany (1993).

    Google Scholar 

  • V. Nissen and H. Paul, A modification of threshold accepting and its application to the quadratic assignment problem, OR Spektrum 17 (1995) 205.

    Article  Google Scholar 

  • A. Nix and M. Vose, Modelling genetic algorithms with Markov chains, Annals of Mathematics and Artificial Intelligence 5 (1992) 88.

    Article  Google Scholar 

  • S. Nolfi, D. Parisi and J.L. Elman, Learning and evolution in neural networks, Adaptive Behavior 3 (1994) 5.

    Google Scholar 

  • B.A. Norman and J.C. Bean, Random keys genetic algorithm for job shop scheduling, Working paper 94-5, Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor (1995).

    Google Scholar 

  • S. Norre, Task scheduling on a multiprocessor system. Deterministic models and stochastic models, RAIRO — Operations Research 28 (1994) 221.

    Google Scholar 

  • E. Nowicki and C. Smutnicki, A fast taboo search algorithm for the job shop problem, Working paper, No. 8/93, Institute of Engineering Cybernetics, Technical University of Wroclaw, Poland (1993).

    Google Scholar 

  • E. Nowicki and C. Smutnicki, A fast taboo search algorithm for the flow shop problem, Working paper, Institute of Engineering Cybernetics, Technical University of Wroclaw, Poland (1994).

    Google Scholar 

  • W.P.M. Nuijten, Time and resource constrained scheduling: A constraint satisfaction approach, Ph.D. Dissertation, Eindhoven University of Technology, Eindhoven, The Netherlands (1994).

    Google Scholar 

  • W.P.M. Nuijten and E.H.L. Aarts, A computational study of constraint satisfaction for multiple capacitated job shop scheduling, European Journal of Operational Research (1995), forthcoming.

  • W.P.M. Nuijten and E.H.L. Aarts, Constraint satisfaction for multiple capacitated job shop scheduling, in:Proceedings of the 11th European Conference on Artificial Intelligence, ed. A. Cohn (Wiley, Chichester, 1994) p. 635

    Google Scholar 

  • W.P.M. Nuijten, E.H.L. Aarts, D.A.A. Van Erp, K.P. Taalman and K.M. Van Hee, Randomized constraint satisfaction for job scheduling, Working paper, Eindhoven University of Technology, Eindhoven, The Netherlands (1993).

    Google Scholar 

  • W.P.M. Nuijten, G.M. Kunnen, E.H.L. Aarts and F.P.M. Dignum, Examination timetabling. A case study for constraint satisfaction, in:Proceedings ECAI'94 Workshop on Constraint Satisfaction Issue Raised by Practical Applications (1994) p. 11.

  • K. Nygard, R. Ficek and R. Sharda, Genetic algorithms, OR/MS Today 19 (1992) 28.

    Google Scholar 

  • K.E. Nygard, P. Juell and N. Kadaba, Neural networks for selecting vehicle routing heuristics, ORSA Journal on Computing 2 (1990) 353.

    Google Scholar 

  • K.E. Nygard and C.-H. Yang, Genetic algorithms for the traveling salesman problem with time windows, in:Computer Science and Operations Research, New Developments in their Interfaces, ed. O. Balci, R. Sharda and S.A. Zenios (Pergamon Press, Oxford, 1992).

    Google Scholar 

  • F.A. Ogbu and D.K. Smith, Simulated annealing for the permutation flowshop problem, Omega 19 (1991) 64.

    Article  Google Scholar 

  • F.A. Ogbu and D.K. Smith, The application of the simulated annealing algorithm to the solution of theN/M/CMAX flowshop problem, Computers and Operations Research 17 (1990) 243.

    Article  Google Scholar 

  • M. Ohlsson, C. Peterson and B. Soderberg, Neural networks for optimization problems with inequality constraints. The knapsack problem, Neural Computation 5 (1993) 331.

    Google Scholar 

  • J.C. Oliveira, J.S. Ferreira and R.V.V. Vidal, Solving real-life combinatorial optimization problems using simulated annealing, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 49.

    Google Scholar 

  • I.M. Oliver, D.J. Smith and J.R.C. Holland, A study of permulation crossover operators on the traveling salesman problem, in:Proceedings of the 2nd International Conference on Genetic Algorithms, ed. J.J. Grefenstette (Lawrence Erlbaum Associates, Pittsburgh, 1987) p. 224.

    Google Scholar 

  • S. Openshaw and L. Rao, Algorithms for reengineering 1991 census geography, Environment and Planning A 27 (1995) 425.

    PubMed  Google Scholar 

  • D. Orvosh and L. Davis, Shall we repair? Genetic algorithms, combinatorial optimization and feasibility constraints, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 650.

    Google Scholar 

  • I.H. Osman, An introduction to metaheuristics, in:Operational Research Tutorial Papers, ed. M. Lawrence and C. Wilson (Operational Research Society Press, Birmingham, 1995a).

    Google Scholar 

  • I.H. Osman, Heuristics for the generalized assignment problem. Simulated annealing and tabu search approaches, OR Spektrum 17 (1995b) 211.

    Article  Google Scholar 

  • I.H. Osman, Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problems, Annals of Operations Research 41 (1993a) 421.

    Article  Google Scholar 

  • I.H. Osman, Heuristics for combinatorial optimization problems. Development and new directions, in:Proceedings of the Second Conference on Information Technology and Applications, ed. K. Hafeez, M.A. Wani and K.S. Jomaa (AMR Publishing, Swansea, 1993b) p. 1.

    Google Scholar 

  • I.H. Osman, Metastrategy simulated annealing and tabu search algorithms for combinatorial optimization problems, Ph.D. Thesis, The Management School, Imperial College, London (1991).

    Google Scholar 

  • I.H. Osman and N. Christofides, Capacitated clustering problems by hybrid simulated annealing and tabu search, International Transactions in Operational Research 1 (1994) 317.

    Article  Google Scholar 

  • I.H. Osman and J.P. Kelly, Metaheuristics. An overview, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996a).

    Google Scholar 

  • I.H. Osman and J.P. Kelly,Metaheuristics. Theory and Applications (Kluwer, Boston, 1996b).

    Google Scholar 

  • I.H. Osman and C.N. Potts, Simulated annealing for permutation flowshop scheduling, Omega 17 (1989) 551.

    Article  Google Scholar 

  • I.H. Osman and S. Salhi, Local search strategies for the mix fleet vehicle routing problem, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • P.R.J. Ostergard, New upper bounds for the football pool problem for 11 and 12 matches, Journal of Combinatorial Theory Series A 67 (1994) 161.

    Article  Google Scholar 

  • A. Ostermeier, An evolutionary strategy with momentum adaptation of the random number distribution, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • R.H.J.M. Otten and L.P.P.P. van Ginneken,The Annealing Algorithm (Kluwer, Boston, 1989).

    Google Scholar 

  • S. Ottner, Genetic algorithms at Channel-4, Expert Systems 11 (1994) 47.

    Google Scholar 

  • R. Padman, Choosing solvers in decision support systems. A neural network application in resource-constrained project scheduling, in:Recent Developments in Decision Support Systems (Springer, Berlin, 1993) p. 559.

    Google Scholar 

  • L. Painton and J. Campbell, Genetic algorithms in optimization of system reliability, IEEE Transactions on Reliability 44 (1995) 172.

    Article  Google Scholar 

  • L. Painton and U. Diwekar, Stochastic annealing for synthesis under uncertainty, European Journal of Operational Research 83 (1995) 489.

    Article  Google Scholar 

  • R. Pakath and J.S. Zaveri, Specifying critical inputs in a genetics driven decision support system: An automated facility, Working paper, Department of Decision Science and Information Systems, College of Business and Economics, University of Kentucky, Lexington (1993).

    Google Scholar 

  • K.F. Pal and D. Bhandari, Selection of optimal set of weights in a layered network using genetic algorithms, Information Sciences 80 (1994) 213.

    Article  Google Scholar 

  • K.F. Pal, Genetic algorithms for the traveling salesman problem based on a heuristic crossover operation, Biological Cybernetics 69 (1993) 539.

    Article  Google Scholar 

  • J. Pannetier, Simulated annealing. An introductory review, Institute of Physics Conference Series 107 (1990) 23.

    Google Scholar 

  • C.H. Papadimitriou, The complexity of the Lin-Kernighan heuristic for the traveling salesman problem, SIAM Journal on Computing 21 (1992) 450.

    Article  Google Scholar 

  • C.H. Papadimitriou and K. Steiglitz,Combinatorial Optimization. Algorithms and Complexity (Prentice-Hall, Englewood Cliffs, 1982).

    Google Scholar 

  • P.M. Pardalos, L. Pitsoulis, T. Mavridou and M.G.C. Resende, Parallel search for combinatorial optimization. Genetic algorithms, simulated annealing and GRASP,Lecture Notes in Computer Science 980 (1995) p. 317.

    Google Scholar 

  • P.M. Pardalos, L.S. Pitsoulis and M.G.C. Resende, A parallel GRASP implementation for the quadratic assignment problem, in:Parallel Algorithms for Irregularly Structured Problems, Irregular'94, ed. A. Ferreira and J. Rolim (Kluwer, Boston, 1995a).

    Google Scholar 

  • P.M. Pardalos, L.S. Pitsoulis and M.G.C. Resende,Fortran subroutines for approximate solution of sparse quadratic assignment problems using GRASP, Working paper, AT&T Bell Laboratories, Murray Hill, New Jersey (1995b).

    Google Scholar 

  • P.M. Pardalos and J. Xue, The maximum clique problem, Journal of Global Optimization 4 (1994) 301.

    Article  Google Scholar 

  • J. Paredis, Exploiting constraints as background knowledge for genetic algorithms. A case study, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • K. Park and B. Carter, On the effectiveness of genetic search in combinatorial optimization, in:Proceedings of the 10th ACM Symposium on Applied Computing, GA and Optimization Track (1995) p. 329.

  • V. Paschos, F. Pekergin and V. Zissimopoulos, Approximating the optimal solution of some hard graph problems by a Boltzmann machine, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 119.

    Google Scholar 

  • J. Paulli, A computational comparison of simulated annealing and tabu search applied to the quadratic assignment problem, Time-New York- 396 (1993) 85.

    Google Scholar 

  • J. Pearl,Heuristics: Intelligent Search Strategies for Computer Problem Solving (Addison-Wesley, Wokingham, England, 1984).

    Google Scholar 

  • J.F. Pekny and D.I. Miller, Exact solution of the no-wait flowshop scheduling problem with a comparison to heuristic methods, Computers and Chemical Engineering 15 (1991) 741.

    Article  Google Scholar 

  • M.P. Pensini, G. Mauri and F. Gardin, Flowshop and TSP,Lecture Notes in Artificial Intelligence 565 (1991) p. 157.

    Google Scholar 

  • S.J. Perantonis and D.A. Karras, An efficient constrained learning algorithm with momentum acceleration, Neural Networks 8 (1995) 237.

    Article  Google Scholar 

  • E. Pesch, Machine learning by schedule decomposition, Working Paper RM 93-045, Faculty of Economics and Business Administration, University of Limburg, The Netherlands (1993).

    Google Scholar 

  • E. Pesch and S. Voß, Applied local search. A prologue. Strategies with memories. Local search in application oriented environment, OR Spektrum 17 (1995) 55.

    Article  Google Scholar 

  • C. Peterson, Solving optimization problems with mean-field methods, Physica A200 (1993) 570.

    Google Scholar 

  • C. Peterson, Parallel distributed approaches to combinatorial optimization. Benchmark studies on travelling salesman problem, Neural Computation 2 (1990) 261.

    Google Scholar 

  • C. Peterson and B. Soderberg, A new method for mapping optimization problems onto neural networks, International Journal of Neural Systems 1 (1989) 3.

    Article  Google Scholar 

  • C. Peterson and J.R. Anderson, Neural network and NP-complete optimization problems. A performance study on the graph bisection problem, Complex Systems 2 (1988) 59.

    Google Scholar 

  • C.B. Petley and M.R. Lutze, A theoretical investigation of a parallel genetic algorithm, in:Proceedings of the 3rd International Conference on Genetic Algorithms, ed. J.D. Schaffer (Morgan Kaufmann, San Mateo, 1989) p. 398.

    Google Scholar 

  • C.B. Petley, M.R. Lutze and J.J. Grefenstette, A parallel genetic algorithm in:Proceedings of the 2nd International Conference on Genetic Algorithms, ed. J.J. Grefenstette (Lawrence Erlbaum Associates, Pittsburgh, 1987) p. 155.

    Google Scholar 

  • E. Peyrol, P. Floquet, L. Pibouleau and S. Domenech, Scheduling and simulated annealing application to a semiconductor circuit fabrication plant, Computers and Chemical Engineering 17 (1993) 39.

    Article  Google Scholar 

  • D.T. Pham and H.H. Onder, A knowledge-based system for optimizing workplace layouts using a genetic algorithm, Ergonomics 35 (1992) 1479.

    Google Scholar 

  • P.R. Philipoom, L.P. Pees and L. Wiegmann, Using neural networks to determine internally set due-date assignments for shop scheduling, Decision Sciences 26 (1995) 2.

    Google Scholar 

  • M. Piccioni, A combined multistart annealing algorithm for continuous global optimization, Computers and Mathematics with Applications 21 (1991) 173.

    Article  Google Scholar 

  • S. Pierre, M.A. Hyppolite, J.-M. Bourjolly and O. Dioume, Topological design of computer communication networks using simulated annealing, Engineering Applications of Artificial Intelligence 8 (1995) 61.

    Article  Google Scholar 

  • H. Pirkul and E. Rolland, New heuristic solution procedures for the uniform graph partitioning problem, in:Computer Science and Operations Research, New Developments in their Interfaces, ed. O. Balci, R. Sharda and S.A. Zenios (Pergamon Press, Oxford, 1992).

    Google Scholar 

  • M. Pirlot, General local search heuristics in combinatorial optimization. A tutorial, Belgian Journal of Operations Research, Statistics and Computer Science 32 (1993) 7.

    Google Scholar 

  • S. Poljak, Integer linear programs and local search for max-cut, SIAM Journal on Computing 24 (1995) 822.

    Article  Google Scholar 

  • J. Popovic, Vehicle routing in the case of uncertain demand. A Bayesian approach, Transportation Planning and Technology 19 (1995) 19.

    Google Scholar 

  • S.C.S. Porto and C.C. Ribeiro, A tabu search approach to task scheduling on heterogeneous processors under precedence constraints, The International Journal of High Speed Computing 7 (1995), forthcoming.

  • C.N. Potts and L.N. van Wassenhove, Single machine tardiness sequencing heuristics, IIE Transactions 23 (1991) 346.

    Google Scholar 

  • J.-Y. Potvin, Genetic algorithms for the travelling salesman problem, Annals of Operations Research 63 (1996) 339.

    Google Scholar 

  • J.-Y. Potvin, Neural networks state-of-the-art survey. The travelling salesman problem, ORSA Journal on Computing 5 (1993) 328.

    Google Scholar 

  • J.-Y. Potvin and S. Bengio, A genetic approach to the vehicle routing problem with time windows, ORSA Journal on Computing (1996), forthcoming.

  • J.-Y. Potvin and F. Guertin, The clustered traveling salesman problem: A genetic approach, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • J.-Y. Potvin, T. Kervahut, B.L. Garcia and J.-M. Rousseau, A tabu search heuristic for the vehicle routing problem with time windows, ORSA Journal on Computing (1996), forthcoming.

  • J.-Y. Potvin and C. Robillard, Clustering for vehicle routing with a competitive neural network, Neurocomputing 8 (1995) 125.

    Article  Google Scholar 

  • J.-Y. Potvin and C. Robillard, Integrating operations research and neural networks for vehicle routing, in:The Impact of Emerging Technologies on Computer Science and Operations Research, ed. S.G. Nash and A. Sofer,Operations Research/Computer Science Interfaces 4 (Kluwer, Boston, 1995).

    Google Scholar 

  • J.-Y. Potvin and J.-M. Rousseau, Constraint directed search for the advanced requesy dial-a-ride problem with service quality constraint, in:Computer Science and Operations Research, New Developments in their Interfaces, ed. O. Balci, R. Sharda and S.A. Zenios (Pergamon Press, Oxford, 1992).

    Google Scholar 

  • M. Prakash and M.N. Murty, A genetic approach for selection of (near) optimal subsets of principal components for discrimination, Pattern Recognition Letters 16 (1995) 781.

    Article  Google Scholar 

  • W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery,Numerical Recipe in C (Fortran orPascal).The Art of Scientific Computing (Cambridge University Press, Cambridge, 1992).

    Google Scholar 

  • C.C. Price and P.M. Shah, Optimization by simulated annealing. Experimental application to quadratic assignment problems, Texas Journal of Science 42 (1990) 215.

    Google Scholar 

  • C. Prins, Two scheduling problems in satellite telecommunications, RAIRO — Operations Research 25 (1991) 341.

    Google Scholar 

  • P. Prosser, Hybrid algorithms for the constraint satisfaction problem, Computational Intelligence 9 (1993) 268.

    Google Scholar 

  • A. Prugelbennet and J.L. Shapiro, Analysis of genetic algorithms using statistical mechanics, Physical Review Letters 72 (1994) 1305.

    Article  PubMed  Google Scholar 

  • J.-F. Puget, On the satisfiability of symmetrical constrained satisfaction problems, Working paper, ILOG SA, Gentilly, France (1993)

    Google Scholar 

  • J.-F. Puget, Object oriented constraint programming for transportation problems, Working paper, ILOG SA, Gentilly, France (1992).

    Google Scholar 

  • A.P. Punnen and Y.P. Aneja, A tabu search algorithm for the resource constrained assignment problem, Journal of the Operational Research Society 46 (1995) 214.

    Google Scholar 

  • A.P. Punnen and Y.P. Aneja, Categorized assignment scheduling. A tabu search approach, Journal of the Operational Research Society 44 (1993) 673.

    Google Scholar 

  • B. Purohit, T. Clark and T. Richards, Techniques for routing and scheduling services on a transmission network, BT Technology Journal 13 (1995) 64.

    Google Scholar 

  • X.F. Qi and F. Palmieri, Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space 1. Basic properties of selection and mutation, IEEE Transactions on Neural Networks 5 (1994a) 102.

    Article  Google Scholar 

  • X.F. Qi and F. Palmieri, Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space 2. Analysis of the diversification role of crossover, IEEE Transactions on Neural Networks 5 (1994b) 120.

    Article  Google Scholar 

  • F. Qian and H. Hirata, A parallel computation based on mean-field theory for combinatorial optimization and Boltzmann machines, Systems and Computers in Japan 24 (1994) 86.

    Google Scholar 

  • T.S. Raghu and C. Rajendran, Due-date setting methodologies based on simulated annealing. An experimental study in a real-life job-shop, International Journal of Production Research, 33 (1995) 2535.

    Google Scholar 

  • S. Rajasekaran and J.H. Reif, Nested annealing. A provable improvement to simulated annealing, Theoretical Computer Science 99 (1992) 157.

    Article  Google Scholar 

  • J. Ramanujam and P. Sadayappan, Mapping combinatorial optimization problems onto neural networks, Information Sciences 82 (1995) 239.

    Article  Google Scholar 

  • R.L. Rao and S.S. Iyengar, Bin packing by simulated annealing, Computers and Mathematics with Applications 27 (1994) 71.

    Article  MathSciNet  Google Scholar 

  • R.L. Rardin and M. Sudit, Paroid search. Generic local combinatorial optimization, Discrete Applied Mathematics 43 (1993) 155.

    Article  Google Scholar 

  • C.P. Ravikumar, Parallel search-and-learn technique for solving large-scale traveling sales-person problems, Knowledge-Based Systems 7 (1994) 169.

    Article  Google Scholar 

  • C.P. Ravikumar, Parallel techniques for solving large-scale traveling salesperson problems, Microprocessors and Microsystems 16 (1992) 149.

    Article  Google Scholar 

  • C.P. Ravikumar and L.M. Patnaik, Performance improvement of simulated annealing algorithms, Computer Systems Science and Engineering 5 (1990) 111.

    Google Scholar 

  • C.P. Ravikumar and N. Vedi, Heuristic and neural algorithms for mapping tasks to a reconfigurable array, Microprocessing and Microprogramming 41 (1995) 137.

    Article  Google Scholar 

  • V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith,Modern Heuristic Search Methods (Wiley, Chichester, 1996).

    Google Scholar 

  • V.J. Rayward-Smith,Applications of Modern Heuristic Methods (Alfred Waller, Henley-on-Thames, 1995a).

    Google Scholar 

  • V.J. Rayward-Smith, A unified approach to tabu search, simulated annealing and genetic algorithms, in:Applications of Modern Heuristic Methods, ed. V.J. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995b).

    Google Scholar 

  • S. Rees and R.C. Ball, Criteria for an optimum simulated annealing schedule for problems of the traveling salesman type, Journal of Physics A — Mathematical and General 20 (1987) 1239.

    Article  Google Scholar 

  • C.R. Reeves, Hybrid genetic algorithms for bin-packing and related problems, Annals of Operations Research 63 (1996a) 371.

    Google Scholar 

  • C.R. Reeves, Modern heuristic techniques, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996b).

    Google Scholar 

  • C.R. Reeves, A genetic algorithm for flowshop sequencing, Computers and Operations Research 22 (1995a) 5.

    Article  Google Scholar 

  • C.R. Reeves, Genetic algorithms and combinatorial optimization, in:Applications of Modern Heuristic Methods, ed. V.J. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995b).

    Google Scholar 

  • C.R. Reeves, Heuristics for scheduling a single-machine subject to unequal job release times, European Journal of Operational Research 80 (1995c) 397.

    Article  Google Scholar 

  • C.R. Reeves,Modern Heuristic Techniques for Combinatorial Problems (Blackwell, Oxford, 1993a).

    Google Scholar 

  • C.R. Reeves, Improving the efficiency of tabu search for machine sequencing problems, Journal of the Operational Research Society 44 (1993b) 375.

    Google Scholar 

  • C.R. Reeves and C. Höln, Integrating local search into genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • C. Rego and C. Roucairol, A parallel tabu search algorithm using ejection chains for the vehicle routing problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • C. Rego and C. Roucairol, Using tabu search for solving a dynamic multiterminal truck dispatching problem, European Journal of Operational Research 83 (1995) 411.

    Article  Google Scholar 

  • R. Rego and C. Roucairol, An efficient implementation of ejection chain procedures for the vehicle routing problem, Working paper RR-94/44, Laboratoire PRiSM, Université de Versailles, France (1994).

    Google Scholar 

  • J. Renaud, G. Laporte and F.F. Boctor, A tabu search for the multi-depot vehicle routing problem, Computers and Operations Research 23 (1996) 229.

    Article  Google Scholar 

  • M.G.C. Resende and T.A. Feo, A GRASP for satisfiability, in:The Second DIMACS Implementation Challenge, ed. M.A. Trick,DIMACS Series on Discrete Mathematics and Theoretical Computer Science (American Mathematical Society, 1995).

  • M.G.C. Resende, P.M. Pardalos and Y. Li,Fortran subroutines for approximate solution of dense quadratic assignment problems using GRASP, ACM Transactions on Mathematical Software (1995), forthcoming.

  • M.G.C. Resende and C.C. Ribeiro, A GRASP for graph planarization, Working paper, AT&T Bell Laboratories, Murray Hill, New Jersey (1995).

    Google Scholar 

  • R.G. Reynolds, Z. Michalewicz and M. Cavaretta, Using cultural algorithms for constraints handling in Genocop, in:Proceedings of the 4th Annual Conference on Evolutionary Programming (San Diego, CA, 1995).

  • T. Richards, Y. Jiang and B. Richards, NG-backmarking. An algorithm for constraint satisfaction, BT Technology Journal 13 (1995) 102.

    Google Scholar 

  • J.T. Richardson, M.R. Palmer and G. Liepins, Some guidelines for genetic algorithms with penalty functions, in:Proceedings of the 3rd International Conference on Genetic Algorithms, ed. J.D. Shaffer (Morgan Kaufmann, San Mateo, 1989) p. 191.

    Google Scholar 

  • B. Robic and J. Silc, Algorithm mapping with parallel simulated annealing, Computers and Artificial Intelligence 14 (1995) 339.

    Google Scholar 

  • G. Robinson and P. McIlroy, Exploring some commercial applications of genetic programming,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • F. Robuste and C.F. Daganzo, Centralized hub-terminal geometric concepts 2. Baggage and extensions, Journal of Transportation Engineering-ASCE 117 (1991) 159.

    Google Scholar 

  • A.F. Rocha, Neural nets. A theory for brains and machines,Lecture Notes in Artificial Intelligence 638 (1992) p. 5.

    Google Scholar 

  • Y. Rochat and E.D. Taillard, Probabilistic diversification and intensification in local search for vehicle routing, Journal of Heuristics 1 (1995) 147.

    Google Scholar 

  • Y. Rochat and F. Semet, A tabu search approach for delivering pet food and flour in Switzerland, Journal of the Operational Research Society 45 (1994) 1233.

    Google Scholar 

  • E. Rolland, Abstract heuristic search methods for graph partitioning, Ph.D. Dissertation, The Ohio State University, Columbus (1991).

    Google Scholar 

  • E. Rolland, D.A. Schilling and J.R. Current, An efficient tabu search procedure for the P-median problem, Working paper, Graduate School of Management, University of California at Riverside, CA (1993).

    Google Scholar 

  • E. Rolland, H. Pirkul and F. Glover, Tabu search for graph partitioning, Annals of Operations Research 63 (1996) 209.

    Google Scholar 

  • H.E. Romelin and R.L. Smith, Simulated annealing for constrained global optimization, Journal of Global Optimization 5 (1994) 101.

    Article  Google Scholar 

  • F. Romeo and A. Sangiovanni-Vincentelli, A theoretical framework for simulated annealing, Algorithmica 6 (1991) 302.

    Article  Google Scholar 

  • J.S. Rose, W.M. Snelgrove and Z.G. Vranesic, Parallel standard cell placement algorithms with quality equivalent to simulated annealing, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 7 (1988) 387.

    Article  Google Scholar 

  • K. Rose, E. Gurewitz and G.C. Fox, Constrained clustering as an optimization method, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993) 785.

    Article  Google Scholar 

  • P. Ross and D. Corne, Comparing genetic algorithms, simulated annealing, and stochastic hillclimbing on timetabling problems,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • P. Rousselragot, N. Kouicem and G. Dreyfus, Error-free parallel implementation of simulated annealing,Lecture Notes in Computer Science 496 (1991) p. 231.

    Google Scholar 

  • P. Rousselragot and G. Dreyfus, A problem independent parallel implementation of simulated annealing. Models and experiments, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 9 (1990) 827.

    Article  Google Scholar 

  • G. Rudolph, Convergence analysis of canonical genetic algorithms, IEEE Transactions on Neural Networks 5 (1994) 96.

    Article  Google Scholar 

  • G. Ruppeiner, J.M. Pedersen and P. Salamon, Ensemble approach to simulated annealing, Journal de Physique I 1 (1991) 455.

    Article  Google Scholar 

  • R.A. Russell, Hybrid heuristics for the vehicle routing problem with time windows, Transportation Science 29 (1995) 156.

    Google Scholar 

  • R.A. Rutenbar, Simulated annealing algorithms. An overview, IEEE Circuits and Devices Magazine 5 (1989) 19.

    Article  Google Scholar 

  • J. Ryan, The depth and width of local minima in discrete solution spaces, Discrete Applied Mathematics 56 (1995) 75.

    Article  Google Scholar 

  • Y.G. Saab, A fast and robust network bisection algorithm, IEEE Transactions on Computers 44 (1995) 903.

    Article  Google Scholar 

  • Y.G. Saab and V. Rao, Combinatorial optimization by stochastic evolution, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 10 (1991a) 525.

    Article  Google Scholar 

  • Y.G. Saab and V. Rao, A stochastic algorithm for circuit bi-partitioning,Lecture Notes in Computer Science 507 (1991b) p. 313.

    Google Scholar 

  • N.M. Sadeh and S.R. Thangiah, Learning to recognize (un)promising simulated annealing runs. Efficient search procedures for job shop scheduling and vehicle routing, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • N.M. Sadeh and Y. Nakakuki, Focused simulated annealing search. An application to job shop scheduling, Annals of Operations Research 63 (1996) 77.

    Google Scholar 

  • A. Sakamoto, X.Z. Liu and T. Shimamoto, A modified genetic channel router, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E77A (1994) 2076.

    Google Scholar 

  • S. Salhi and M. Sari, A heuristic approcah for the multi-depot vehicle fleet mix problem, Working paper, School of Mathematics and Statistics, University of Birmingham, England (1995).

    Google Scholar 

  • G.H. Sasaki and B. Hajek, The time complexity of maximum matching by simulated annealing, Journal of the Association for Computing Machinery 35 (1988) 387.

    Google Scholar 

  • T. Satake, K. Morikawa and N. Nakamura, Neural network approach for minimizing the make-span of the general job shop, International Journal of Production Economics 33 (1994) 67.

    Article  Google Scholar 

  • T. Satoh and K. Nara, Maintenance scheduling by using simulated annealing method, IEEE Transactions on Power Systems 6 (1991) 850.

    Article  Google Scholar 

  • J.E. Savage and M.G. Wloka, Parallelism in graph partitioning, Journal of Parallel and Distributed Computing 13 (1991) 257.

    Article  Google Scholar 

  • J.D. Schaffer,Proceedings of the Third International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1989).

    Google Scholar 

  • J.D. Schaffer, R.A. Carnana and L.J. Eshelman, Combinations of genetic algorithms and neural networks. A survey of the state of the art, in:Proceedings of the International Workshops on Combinations of Genetic Algorithms and Neural Networks, COGANN-92 (1992) p. 1.

    Article  Google Scholar 

  • J.D. Schaffer and L.J. Eshelman, Combinatorial optimization by genetic algorithms: The value of the genotype/phenotype distinction, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • W.T. Scherer and F. Rotman, Combinatorial optimization techniques for spacecraft scheduling automation, Annals of Operations Research 50 (1994) 525.

    Article  Google Scholar 

  • L.J. Schmitt, An empirical computational study of genetic algorithms to solve order based problems. An emphasis on TSP and VRPTC, Ph.D. Dissertation, Department of Management Information Systems and Decision Sciences, University of Memphis, Tennessee (1994).

    Google Scholar 

  • L.J. Schmitt and M.M. Amini, Genetic algorithmic approaches to travelling salesman problem. Design and configuration issues, Working paper, Department of Information Technology Management, Christian Brothers University, Memphis, Tennessee (1995a).

    Google Scholar 

  • L.J. Schmitt and M.M. Amini, Performance characteristics of alternative genetic algorithmic approaches to the travelling salesman problem: A rigorous empirical study, Working paper, Department of Information Technology Management, Christian Brothers University, Memphis, Tennessee (1995b).

    Google Scholar 

  • A. Schober, M. Thuerk and M. Eigen, Optimization by hierarchical mutant production, Biological Cybernetics 69 (1993) 493.

    Article  PubMed  Google Scholar 

  • M. Schoenauer and S. Xanthakis, Constrained GA optimization, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 573.

    Google Scholar 

  • A.C. Schultz, J.J. Grefenstette and K.A. DeJong, Test and evaluation by genetic algorithms, IEEE Expert-Intelligent Systems and Their Applications 8 (1993) 9.

    Google Scholar 

  • L.L. Schumaker, Computing optimal triangulations using simulated annealing, Computer-Aided Geometric Design 10 (1993) 329.

    Article  Google Scholar 

  • H.-P. Schwefel,Numerical Optimization for Computer Models (Wiley, Chichester, 1981).

    Google Scholar 

  • H.-P. Schwefel and R. Männer,Parallel Problem Solving From Nature, PPSN 1 Proceedings, Lecture Notes in Computer Science 496 (Springer, Berlin, 1991).

    Google Scholar 

  • R.S. Segall, Some mathematical and computer modeling of neural networks, Applied Mathematical Modelling 19 (1995) 386.

    Article  Google Scholar 

  • D.A. Sekharan and R.L. Wainwright, Manipulating subpopulations in genetic algorithms for solving theK-way graph partitioning problem, in:Proceedings of the 7th Oklahoma Symposium on Artificial Intelligence (Stillwater, 1993) p. 215.

  • D.A. Sekharan and R.L. Wainwright, Manipulating subpopulations of feasible and infeasiblesolutions in genetic algorithms, in: Proceedings of the ACM/SIGAPP Symposium on Applied Computing (Indianapolis, 1993) p. 118.

  • S.Z. Selim and K. Alsultan, A simulated annealing algorithm for the clustering problem, Pattern Recognition 24 (1991) 1003.

    Article  Google Scholar 

  • S. Selvakumar and C.S.R. Murthy, An efficient heuristic algorithm for mapping parallel programs onto multicomputers, Microprocessing and Microprogramming 36 (1993) 83.

    Article  Google Scholar 

  • F. Semet and E.D. Taillard, Solving real-life vehicle routing problems efficiently using tabu search, Annals of Operations Research 41 (1993) 469.

    Article  Google Scholar 

  • K. Shahookar and P. Mazumder, VLSI cell placement techniques, Computing Surveys 23 (1991) 143.

    Article  Google Scholar 

  • J.S. Shang, Multicriteria facility layout problem. An integrated approach, European Journal of Operational Research 66 (1993) 291.

    Article  Google Scholar 

  • B.A. Shapiro and J. Navetta, A massively parallel genetic algorithm for RNA secondary structure prediction, Journal of Supercomputing 8 (1994) 195.

    Article  Google Scholar 

  • J.A. Shapiro and A.S. Alfa, An experimental analysis of the simulated annealing algorithm for a single-machine scheduling problem, Engineering Optimization 24 (1995) 79.

    Google Scholar 

  • Y.M. Sharaiha, M. Gendreau, G. Laporte and I.H. Osman, A tabu search algorithm for the capacitated minimum spanning tree problem, Working paper, CRT-95-79, Centre de recherche sur les transports, Montréal (1995).

    Google Scholar 

  • R. Sharda, Neural networks for the MS/OR analyst. An application bibliography, Interfaces 24 (1994) 116.

    Google Scholar 

  • R. Sharda, Statistical applications of neural networks, Intelligent Systems Report 8 (1991) 12.

    Google Scholar 

  • R. Sharda, Neural nets for operations research, Intelligent Systems Report 9 (1992) 14.

    Google Scholar 

  • R. Sharda and R. Patil, Connectionist approach to time series prediction. An empirical test, Journal of Intelligent Manufacturing 3 (1992) 317.

    Article  Google Scholar 

  • P.K. Sharpe, A.G. Chalmers and A. Greenwood, Genetic algorithms for generating minimum path configurations, Microprocessors and Microsystems 19 (1995) 9.

    Article  Google Scholar 

  • R. Sharpe and B.S. Marksjo, Solution of the facilities layout problem by simulated annealing, Computers Environment and Urban Systems 11 (1986) 147.

    Article  Google Scholar 

  • G.B. Sheble and K. Brittig, Refined genetic algorithm. Economic dispatch example, IEEE Transactions on Power Systems 10 (1995) 117.

    Article  Google Scholar 

  • S. Shekhar and M.B. Amin, Generalization by neural networks, IEEE Transactions on Knowledge and Data Engineering 4 (1992) 177.

    Article  Google Scholar 

  • R.P. Sheridan and S.K. Kearsley, Using a genetic algorithm to suggest combinatorial libraries, Journal of Chemical Information and Computer Sciences 35 (1995) 310.

    Article  Google Scholar 

  • P.H. Shih and W.S. Feng, An analog neural network approach to global routing problem, Cybernetics and Systems 22 (1991) 747.

    MathSciNet  Google Scholar 

  • F. Shiratani and K. Yamamoto, Combinatorial optimization by using a neural network operating in block-sequential mode, Systems and Computers in Japan 25 (1994) 103.

    Google Scholar 

  • R. Sikora and M. Shaw, A double layered learning approach to acquiring rules for classification. Integrating genetic algorithms with similarity based learning, ORSA Journal on Computing 6 (1994) 174.

    Google Scholar 

  • A. Silver, R.V.V. Vidal and D. de Werra, A tutorial on heuristic methods, European Journal of Operational Research 5 (1980) 153.

    Article  Google Scholar 

  • P.D. Simic, Statistical mechanics as the underlying theory of the elastic and neural optimizations, IEEE Transactions on Neural Networks 1 (1990) 89.

    Google Scholar 

  • M.W. Simmen, Parameter sensitivity of the elastic net approach to the travelling salesman problem, Neural Computation 3 (1991) 363.

    Google Scholar 

  • A.R. Simpson, G.C. Dandy and L.J. Murphy, Genetic algorithms compared to other techniques for pipe optimization, Journal of Water Resources Planning and Management — ASCE 120 (1994) 423.

    Google Scholar 

  • M. Sinclair, Comparison of the performance of modern heuristics for combinatorial optimization on real data, Computers and Operations Research 20 (1993) 687.

    Article  Google Scholar 

  • G.S. Singh and K.R. Deshpande, On fast load partitioning by simulated annealing and heuristic algorithms for general-class of problems, Advances in Engineering Software 16 (1993) 23.

    Article  Google Scholar 

  • D.J. Sirag and P.T. Weisser, Towards a unified thermodynamic genetic operator, in:Proceedings of the 2nd International Conference on Genetic Algorithms, ed. J.J. Grefenstette (Lawrence Erlbaum Associates, Pittsburgh 1987) p. 116.

    Google Scholar 

  • A.J. Skinner and J.Q. Broughton, Neural networks in computational materials science. Training algorithms, Modelling and Simulation in Materials Science and Engineering 3 (1995) 371.

    Article  Google Scholar 

  • J. Skorin-Kapov, Extensions of a tabu search adaptation to the quadratic assignment problem, Computers and Operations Research 21 (1994) 855.

    Article  MathSciNet  Google Scholar 

  • J. Skorin-Kapov, Tabu search applied to the quadratic assignment problem, ORSA Journal on Computing 2 (1990) 33.

    Google Scholar 

  • J. Skorin-Kapov and J.-F. Labourdette, On minimum congestion routing in rearrangeable multi-hop lightwave networks, Journal of Heuristics 1 (1995) 129.

    Google Scholar 

  • D. Skorin-Kapov and J. Skorin-Kapov, On tabu search for the location of interacting hub facilities, European Journal of Operational Research 73 (1994) 502.

    Article  Google Scholar 

  • J. Skorin-Kapov and A.J. Vakharia, Scheduling a flow-line manufacturing cell. A tabu search approach, International Journal of Production Research 31 (1993) 1721.

    Google Scholar 

  • R. Slowinski, B. Soniewicki and J. Weglarz, DSS for multiobjective project scheduling, European Journal of Operational Research 79 (1994) 220.

    Article  Google Scholar 

  • B.M. Smith, S.C. Brailsford, P.M. Hubbard and H.P. Williams, The progressive party problem. Integer linear programming and constraint programming compared, Working paper, Division of Artificial Intelligence, School of Computer Studies, University of Leeds, UK (1995).

    Google Scholar 

  • D. Smith, Bin packing with adaptive search, in:Proceedings of an International Conference on Genetic Algorithms, ed. J.J. Grefenstette (Lawrence Erlbaum Associates, Pittsburgh, 1985) p. 202.

    Google Scholar 

  • J. Smith and T.C. Fogarty, An adaptive poly-parental recombination strategy,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • K. Smith, M. Krishnamoorthy and M. Palaniswami, Traditional heuristic versus neural approaches to a car sequencing problem, Working paper, CSIRO Division of Mathematics and Statistics, Clayton, Australia (1994).

    Google Scholar 

  • S. Smith and T.A. Feo, A GRASP for coloring sparse graphs, Working paper, Operations Research Group, Department of Mechanical Engineering, The University of Texas at Austin (1994).

  • D.G. So and K.A. Dowsland, Simulated annealing: An application to simulation optimisation, Working paper, University of Wales, Swansea, Statistics and Operational Research Group, European Business Management School, Swansea (1993).

    Google Scholar 

  • S. Sofianopoulou, A queueing network application to a telecommunications distributed system, RAIRO — Operations Research 26 (1992a) 409.

    Google Scholar 

  • S. Sofianopoulou, Simulated annealing applied to the process allocatio problem, European Journal of Operational Research 60 (1992b) 327.

    Article  Google Scholar 

  • A. Sohn, Generalized speculative computation of parallel simulated annealing, Annals of Operations Research 63 (1996) 29.

    Google Scholar 

  • A. Sohn, ParallelN-ary speculative computation of simulated annealing, IEEE Transactions on Parallel and Distributed Systems 6 (1995) 997.

    Article  Google Scholar 

  • L. Sondergeld and S. Voß, A star-shaped diversification approach in tabu search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • L. Song and A. Vannelli, A VLSI placement method using tabu search, Microelectronics Journal 23 (1992) 167.

    Article  Google Scholar 

  • P. Soriano and M. Gendreau, Diversification strategies in tabu search algorithm for the maximum clique problem, Annals of Operations Research 63 (1996) 189.

    Google Scholar 

  • G.B. Sorkin, Efficient simulated annealing on fractal energy landscapes, Algorithmica 6 (1991) 367.

    Article  Google Scholar 

  • A. Souilah, Simulated annealing for manufacturing systems layout design, European Journal of Operational Research 82 (1995) 592.

    Article  Google Scholar 

  • S.B. Spalti and T.M. Liebling, Modeling the satellite placement problem as a network flow problem with one side constraint, OR Spektrum 13 (1991) 1.

    Article  Google Scholar 

  • W.M. Spears and V. Anand, A study of crossover operators in genetic programming,Lecture Notes in Artificial Intelligence 542 (1991) p. 409.

    Google Scholar 

  • R. Srichander, Efficient schedules for simulated annealing, Engineering Optimization 24 (1995) 161.

    Google Scholar 

  • J. Sridhar and C. Rajendran, Scheduling in a cellular manufacturing system. A simulated annealing approach, International Journal of Production Research 31 (1993) 2927.

    Google Scholar 

  • R. Srikanth, R. George, N. Warsi, D. Prabhu, F.E. Petry and B.P. Buckles, A variable length genetic algorithm for clustering and classification, Pattern Recognition Letters 16 (1995) 789.

    Article  Google Scholar 

  • M. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation 2 (1995) 221.

    Google Scholar 

  • M. Srinivas and L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 24 (1994a) 656.

    Google Scholar 

  • M. Srinivas and L.M. Patnaik, Genetic algorithms. A survey, Computer 27 (1994b) 17.

    Article  Google Scholar 

  • B. Srivastava and W.H. Chen, Part type selection problem in flexible manufacturing systems: Tabu search algorithms, Annals of Operations Research 41 (1993) 279.

    Article  Google Scholar 

  • P.F. Stadler, Correlation in landscapes of combinatorial optimization problems, Europhysics Letters 20 (1992) 479.

    Google Scholar 

  • J. Stander and B.W. Silverman, Temperature schedules for simulated annealing, Statistics and Computing 4 (1994) 21.

    Article  Google Scholar 

  • J.P.P. Starink and E. Backer, Finding point correspondences using simulated annealing, Pattern Recognition 28 (1995) 231.

    Article  Google Scholar 

  • T. Starkweather, D. Whitley, K. Mathias and S. McDaniel, Sequence scheduling with genetic algorithms for the travelling salesman problem, in:New Directions for Operations Research in Manufacturing, ed. G. Fandel, T. Gulledge and A. Jones (Springer, Berlin, 1992).

    Google Scholar 

  • T. Starkweather, D. Whitley and K. Mathias, Optimization using distributed genetic algorithms,Lecture Notes in Computer Science 496 (1991) p. 165.

    Google Scholar 

  • J. Stender,Parallel Genetic Algorithms. Theory and Applications (ISO Press, Amsterdam, 1992).

    Google Scholar 

  • B.S. Stewart, C.F. Liaw and C.C. White, A bibliography of heuristic search research through 1992, IEEE Transactions on Systems, Man and Cybernetics 24 (1994) 268.

    Google Scholar 

  • W.R. Stewart, J.P. Kelly and M. Laguna, Solving vehicle routing problems using generalised assignments and tabu search, Working paper, Graduate School of Business, University of Colorado, Boulder (1994).

    Google Scholar 

  • G.S. Stiles, The effect of numerical precision upon simulated annealing, Physics Letters A 185 (1994) 253.

    Article  Google Scholar 

  • D.J. Stockton and L. Quinn, Aggregate production planning using genetic algorithms, Journal of Engineering Manufacture 209 (1995) 201.

    Google Scholar 

  • R.H. Storer, S.W. Flanders and S.D. Wu, Problem space local search for number partitioning, Annals of Operations Research 63 (1996) 465.

    Google Scholar 

  • R.H. Storer, S.D. Wu and R. Vaccari, Local search in problem and heuristic space for job shop scheduling, ORSA Journal on Computing 7 (1995) 453.

    Google Scholar 

  • R.H. Storer, S.D. Wu and R. Vaccari, New search spaces for sequencing problems with application to job shop scheduling, Management Science 38 (1992) 1495.

    Google Scholar 

  • P.N. Strenski and S. Kirkpatrick, Analysis of finite length annealing schedules, Algorithmica 6 (1991) 346.

    Article  Google Scholar 

  • B.E. Stuckman and E.E. Easom, A comparison of Bayesian sampling global optimization techniques, IEEE Transactions on Systems, Man and Cybernetics 22 (1992) 1024.

    Google Scholar 

  • Y. Sugai and H. Hirata, Hierarchical algorithm for a partition problem using simulated annealing. Application to placement in VLSI layout, International Journal of Systems Science 22 (1991) 2471.

    MathSciNet  Google Scholar 

  • P.N. Suganthan, E.K. Teoh and D.P. Mital, Self-organizing Hopfield network for attributed relational graph matching, Image and Vision Computing 13 (1995) 61.

    Article  Google Scholar 

  • D.K. Sun, R. Batta and L. Lin, Effective job shop scheduling through active chain manipulation, Computers and Operations Research 22 (1995) 159.

    Article  Google Scholar 

  • D.K. Sun, L. Lin and R. Batta, Cell-formation using tabu search, Computers and Industrial Engineering 28 (1995) 485.

    Article  Google Scholar 

  • L.X. Sun, F. Xu, Y.Z. Liang, Y.L. Xie and R.Q. Yu, Cluster analysis by thek-means algorithm and simulated annealing, Chemometrics and Intelligent Laboratory Systems 25 (1994) 51.

    Article  Google Scholar 

  • L.X. Sun, Y.I. Xie, X.H. Song, J.H. Wang and R.Q. Yu, Cluster-analysis by simulated annealing, Computers and Chemistry 18 (1994) 103.

    Article  Google Scholar 

  • M. Sun and P.G. McKeown, Tabu search applied to the general fixed charge problem, Annals of Operations Research 41 (1993) 405.

    Article  Google Scholar 

  • T. Sun, P. Meakin and T. Jossang, A fast optimization method based on a hierarchical strategy for the traveling salesman problem, Physica A 199 (1993) 232.

    Google Scholar 

  • W.J. Sun and C. Sechen, Efficient and effective placement for very large circuits, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 14 (1995) 349.

    Article  Google Scholar 

  • G. Suresh and S. Sahu, Stochastic assembly line balancing using simulated annealing, International Journal of Production Research 32 (1994) 2249.

    Google Scholar 

  • G. Suresh and S. Sahu, Multiobjective facility layout using simulated annealing, International Journal of Production Economics 32 (1993) 239.

    Article  Google Scholar 

  • P.D. Surry, N.J. Radcliffe and I.D. Boyd, A multi-objective approach to constrained optimisation of gas supply networks. The COMOGA method,Lecture Notes in Computer Science 993 (1995), forthcoming.

  • P. Sutton and S. Boyden, Genetic algorithms. A general search procedure, American Journal of Physics 62 (1994) 549.

    Article  Google Scholar 

  • J. Suzuki, A Markov chain analysis on simple genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 655.

    Google Scholar 

  • G. Syswerda, Schedule optimization using genetic algorithms, in:Handbook of Genetic Algorithms, ed. L. Davis (Van Nostrand Reinhold, New York, 1991).

    Google Scholar 

  • G. Syswerda, Uniform crossover genetic algorithms, in:Proceedings of the 3rd International Conference on Genetic Algorithms, ed. J.D. Schaffer (Morgan Kaufmann, San Mateo, 1989) p. 2.

    Google Scholar 

  • H. Szu and S. Foo, Divide-and-conquer orthogonality principle for parallel optimizations in TSP, Neurocomputing 8 (1995) 249.

    Article  Google Scholar 

  • H. Szu and R. Hartley, Fast simulated annealing, Physics Letters A122 (1987) 157.

    Google Scholar 

  • S. Szykman and J. Cagan, A simulated annealing based approach to 3-dimensional component packing, Journal of Mechanical Design 117 (1995) 308.

    Google Scholar 

  • R. Tadei, F. DellaCroce and G. Menga, Advanced search techniques for the job-shop problem. A comparison, RAIRO — Operations Research 29 (1995) 179.

    Google Scholar 

  • E.D. Taillard, Comparison of iterative searches for the quadratic assignment problem, Location Science 3 (1995) 87.

    Article  Google Scholar 

  • E.D. Taillard, Parallel taboo search techniques for the job shop scheduling problem, ORSA Journal on Computing 6 (1994) 108.

    Google Scholar 

  • E.D. Taillard, Parallel iterative search methods for vehicle routing problems, Networks 23 (1993a) 661.

    Google Scholar 

  • E.D. Taillard, Recherches iterativés dirigées parallèles, Ph.D. Dissertation, Department of Mathématiques, Ecole Polytechnique de Lausanne, Switzerland (1993b).

    Google Scholar 

  • E.D. Taillard, Robust taboo search for the quadratic assignment problem, Parallel Computing 17 (1991) 443.

    Article  Google Scholar 

  • T. Takada, K. Sanou and S. Fukumara, A neural-network system for solving an assortment problem in the steel industry, Annals of Operations Research 57 (1995) 265.

    Article  Google Scholar 

  • Y. Takefuji and K.C. Lee, A near-optimum parallel planarization algorithm, Science 245 (1989) 1221.

    Google Scholar 

  • Y. Takefuji and J. Wang,Neural Networks for Optimization and Combinatorics (World Scientific, Singapore, 1994).

    Google Scholar 

  • K.Y. Tam, A simulated annealing algorithm for allocating space to manufacturing cells, International Journal of Production Research 30 (1992a) 63.

    Google Scholar 

  • K.Y. Tam, Genetic algorithms, function optimization, and facility layout design, European Journal of Operational Research 63 (1992b) 322.

    Article  Google Scholar 

  • K.Y. Tam and M.Y. Kiang, Managerial applications of neural networks. The case of bank failure predictions, Management Science 38 (1992c) 926.

    Google Scholar 

  • H. Tamaki, M. Mori, M. Araki, Y. Mishima and H. Ogai, Multi-criteria optimization by genetic algorithms. A case of scheduling in hot rolling process, in:Proceedings of the 3rd Conference of the Association of Asian-Pacific Operational Research Societies within IFORS (APORS'94, 1994) p. 374.

  • G. Tambouratzis, Optimizing the clustering performance of a self-organizing logic neural-network with topology-preserving capabilities, Pattern Recognition Letters 15 (1994) 1019.

    Article  Google Scholar 

  • T. Tanaka, T. Higuch and T. Furuya, An efficient algorithm for solving optimization problems on Hopfield-type neural networks, Systems and Computers in Japan 26 (1995) 73.

    Google Scholar 

  • M. Tandon, P.T. Cummings and M.D. Levan, Scheduling of multiple products on parallel units with tardiness penalties using simulated annealing, Computers and Chemical Engineering 19 (1995) 1069.

    Article  Google Scholar 

  • M. Tandon, P.T. Cummings and M.D. Levan, Flowshop sequencing with non-permutation schedules, Computers and Chemical Engineering 15 (1991) 601.

    Article  Google Scholar 

  • M. Taneja, S.M. Sharma and N. Viswanadham, Location of quality control stations in manufacturing systems. A simulated annealing approach, Systems Practice 7 (1994) 367.

    Google Scholar 

  • R. Tanese, Distributed genetic algorithms for function optimization, Ph.D. Dissertation, The University of Michigan, Ann Arbor (1989).

    Google Scholar 

  • L.X. Tao and Y.C. Zhao, Effective heuristic algorithms for VLSI circuit partition, IEE Proceedings — G: Circuits, Devices and Systems 140 (1993a) 127.

    Google Scholar 

  • L.X. Tao and Y.C. Zhao, Multiway graph partition by stochastic probe, Computers and Operations Research 20 (1993b) 321.

    Article  Google Scholar 

  • L.X. Tao, Y.C. Zhao, K. Thulasiraman and M.N.S. Swamy, Simulated annealing and tabu search algorithms for multiway graph partition, Journal of Circuits, Systems and Computers 2 (1992) 159.

    Google Scholar 

  • D.M. Tate and A.E. Smith, A genetic approach to the quadratic assignment problem, Computers and Operations Research 22 (1995) 73.

    Article  Google Scholar 

  • D.M. Tate and A.E. Smith, Genetic optimization using a penalty function, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 499.

    Google Scholar 

  • A. Taudes and T. Netousek, Implementing branch and bound algorithms on a cluster of work-stations. A survey, some new results and open problems,Lecture Notes in Economics and Mathematical Systems 367 (1991) p. 79.

    Google Scholar 

  • J.G. Taylor,Neural Networks (Alfred Waller, Henley-on-Thames, 1995).

  • J.G. Taylor,Mathematical Approaches to Neural Networks (North-Holland, Amsterdam, 1993).

    Google Scholar 

  • H.M.M. ten Eikelder, M.G.A. Verhoeven, T.W.M. Vossen and E.H.L. Aarts, A probabilistic analysis of local search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • D. Teodorovic and G. Pavkovic, A simulated annealing technique approach to the vehicle routing problem in the case of stochastic demand, Transportation Planning and Technology 16 (1992) 261.

    Google Scholar 

  • P.B. Thanedar and G.N. Vanderplaats, Survey of discrete variable optimization for structural design, Journal of Structural Engineering-ASCE 121 (1995) 301.

    Article  Google Scholar 

  • S.R. Thangiah, Vehicle routing with time windows using genetic algorithms, in:Applications Handbook of Genetic Algoriothms. New Frontiers, Vol. 2 (CRC Press, Florida, 1995).

    Google Scholar 

  • S.R. Thangiah and A.V. Gubbi, Effect of genetic sectoring on vehicle routing problems with time windows, Working paper, SRU-CpSc-TR-92-13, Computer Science Department, Slippery Rock University, PA (1992).

    Google Scholar 

  • S.R. Thangiah and K.E. Nygard, MICAH. A genetic algorithm system for multi-commodity transshipment problems,Proccedings of the 8th IEEE Conference on Artificial Intelligence for Applications, Monterey, CA (1992a) p. 240.

    Google Scholar 

  • S.R. Thangiah, K.E. Nygard, School bus routing using genetic algorithm, in:Proceedings of the SPIE Conference on Applications of Artificial Intelligence, X: Knowledge Based Systems, Orlando, Florida (1992b) p. 387.

    Google Scholar 

  • S.R. Thangiah, K.E. Nygard, Dynamic trajectory routing using an adaptive search strategy, Working paper SRU-CpSc-TR-92-20, Computer Science Department, Slippery Rock University, PA (1992c).

    Google Scholar 

  • S.R. Thangiah, K.E. Nygard and P.L. Juell, GIDEON. A genetic algorithm system for vehicle routing with time windows, in:Proceedings of the 7th IEEE Conference on Artificial Intelligence Applications, Miami, FL (1991) p. 322.

  • S.R. Thangiah, I.H. Osman and T. Sun, Metaheuristics for vehicle routing problems with time windows, Working paper UKC/IMS/OR94/8, Institute of Mathematics and Statistics, University of Kent, Canterbury (1994).

    Google Scholar 

  • S.R. Thangiah, I.H. Osman, R. Vinayagamoorthy and T. Sun, Algorithms for vehicle routing problems with time deadlines, American Journal of Mathematical and Management Sciences 13 (1993) 323.

    Google Scholar 

  • V.E. Theodoracatos and J.L. Grimsley, The optimal packing of arbitrarily-shaped polygons using simulated annealing and polynomial-time cooling schedules, Computer Methods in Applied Mechanics and Engineering 125 (1995) 53.

    Article  MathSciNet  Google Scholar 

  • J. Thiel and S. Voß, Some experiences on solving multiconstraint zero-one knapsack problems with genetic algorithms, INFOR 32 (1994) 226.

    Google Scholar 

  • G.M. Thompson, A simulated annealing heuristic for shift scheduling using non-continuously available employees, Computers and Operations Research 23 (1996) 275.

    Article  Google Scholar 

  • J.M. Thompson and K.A. Dowsland, Variants of simulated annealing for the examination timetabling problem, Annals of Operations Research 63 (1996) XXX

    MathSciNet  Google Scholar 

  • K.W. Tindell, A. Burns and A.J. Wellings, Allocating hard real-time tasks. An NP-hard problem made easy, Real-Time Systems 4 (1992) 145.

    Article  Google Scholar 

  • V. Todorov, Computing the minimum covariance determinant estimator by simulated annealing, Computational Statistics and Data Analysis 14 (1992) 515.

    Article  Google Scholar 

  • P. Toth and D. Vigo, Heuristic algorithms for the handicapped persons for transportation problem, Working paper DEIS-OR-95-7, D.E.I.S., Università di Bologna, Italy (1995).

    Google Scholar 

  • M. Toulouse, T.G. Crainic and M. Gendreau, Communication issues in designing cooperative multi-thread parallel searches, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • G.G. Towell and J.W. Shavlik, Knowledge-based artificial neural networks, Artificial Intelligence 70 (1994) 119.

    Article  Google Scholar 

  • M. Toyonaga, C. Iwasaki, Y. Sawada and T. Akino, A multilayer channel router using simulated annealing, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E77A (1994) 2085.

    Google Scholar 

  • A. Trouve, Asymptotical behavior of several interacting annealing processes, Probability Theory and Related Fields 102 (1995) 123.

    Article  Google Scholar 

  • A. Trouve, Partially parallel simulated annealing. Low and high temperature approach of the invariant measure,Lecture Notes in Control and Information Sciences 177 (1992a) p. 262.

    Google Scholar 

  • A. Trouve, Optimal convergence rate for generalized simulated annealing, Comptes Rendus de l'Academie des Sciences Série I — Mathématique 315 (1992b) 1197.

    Google Scholar 

  • E.P.K. Tsang,Foundations of Constraint Satisfaction (Academic Press, London, 1993).

    Google Scholar 

  • E.P.K. Tsang, Scheduling techniques. A comparative study, BT Technology Journal 13 (1995) 16.

    Google Scholar 

  • E.P.K. Tsang and C.J. Wang, A generic neural network approach for constraint satisfaction problems, in:Neural Network Applications, ed. J.G. Taylor (Springer, Berlin, 1992) p. 12.

    Google Scholar 

  • J.N. Tsitsiklis, Markov chains with rare transitions and simulated annealing, Mathematics of Operations Research 14 (1989) 70.

    Google Scholar 

  • Y. Tsujimura, M. Gen and E. Kubota, Solving fuzzy assembly-line balancing problem with genetic algorithms, Computers and Industrial Engineering 29 (1995) 543.

    Article  Google Scholar 

  • E.E. Tucker, Exchange of comments on convergence of generalized simulated annealing with variable step size with application toward parameter estimations of linear and nonlinear models, Analytical Chemistry 64 (1992) 1199.

    Article  Google Scholar 

  • B.C.H. Turton, Optimization of genetic algorithms using the Taguchi method, Journal of Systems Engineering 4 (1994) 121.

    Google Scholar 

  • D. Tuyttens, M. Pirlot, J. Teghem, E. Trauwaert and B. Liégeois, Homogeneous grouping of nuclear fuel cans through simulated annealing and tabu search, Annals of Operations Research 50 (1994) 525.

    Article  Google Scholar 

  • G.J. Udo and Y.P. Gupta, Applications of neural networks in manufacturing management-systems, Production Planning and Control 5 (1994) 258.

    Google Scholar 

  • T. Ueda, K. Takahashi, C.Y. Ho and S. Mori, The scheduling of the parameters in Hopfield neural networks with fuzzy control, IEICE Transactions on Information and Systems E77 (1994) 895.

    Google Scholar 

  • N.L.J. Ulder, E.H.L. Aarts, H.J. Bandelt, P.J.M. van Laarhoven and E. Pesch, Genetic local search algorithms for the traveling salesman problem,Lecture Notes in Computer Science 496 (1991) p. 109.

    Google Scholar 

  • K. Urahama, Analog method for solving combinatorial optimization problems, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E77A (1994) 302.

    Google Scholar 

  • R.J.M. Vaessens, E.H.L. Aarts and J.H. Vanlint, Genetic algorithms in coding theory. A table for A3(N,D), Discrete Applied Mathematics 45 (1993) 71.

    Article  Google Scholar 

  • R.J.M. Vaessens, E.H.L. Aarts and J.K. Lenstra, A local search template, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • S. Vaithyanathan and J.P. Ignizio, A stochastic neural network for resource constrained scheduling, Computers and Operations Research 19 (1992) 241.

    Article  Google Scholar 

  • A.J. Vakharia and Y.L. Chang, A simulated annealing approach to scheduling a manufacturing cell, Naval Research Logistics 37 (1990) 559.

    MathSciNet  Google Scholar 

  • A.I. Vakhutinsky and B.L. Golden, A hierarchical strategy for solving traveling salesman problem using elastic nets, Journal of Heuristics 1 (1995) 67.

    Google Scholar 

  • V. Valls, R. Martí and P. Lino, A tabu thresholding algorithm for arc crossing minimization in bipartite graphs, Annals of Operations Research 63 (1996) 233.

    Google Scholar 

  • V. Valls, M.A. Pérez and M.S. Quintanilla, A modified tabu thresholding approach for the generalised restricted vertex colouring problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • A. van Breedam, An analysis of the behavior of heuristics for the vehicle routing problem for selection of problems with vehicle-related, customer-related and time-related constraints, Ph.D. Dissertation, Faculty of Applied Economics, University of Antwerp, Belgium (1994).

    Google Scholar 

  • A. van Breedam, Improvement heuristics for the vehicle routing problem based on simulated annealing, European Journal of Operational Research 86 (1995) 480.

    Article  Google Scholar 

  • D.E. van de Bout and T.K. Miller, Graph partitioning using annealed neural networks, IEEE Transactions on Neural Networks 1 (1990) 192.

    Article  Google Scholar 

  • D.E. van de Bout and T.K. Miller, Improving the performance of the Hopfield Tank neural network through normalization and annealing, Biological Cybernetics 62 (1988) 129.

    Google Scholar 

  • L.J.J. van Derbruggen, J.K. Lenstra and P.C. Schuur, Variable-depth search for the single-vehicle pickup and delivery problem with time windows, Transportation Science 27 (1993) 298.

    Google Scholar 

  • R. van Driessche and R. Piessens, Load balancing with genetic algorithms, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (North-Holland, Amsterdam, 1992).

    Google Scholar 

  • P.J.M. van Laarhoven, E.H.L. Aarts and J.K. Lenstra, Job shop scheduling by simulated annealing, Operations Research 40 (1992) 113.

    Google Scholar 

  • P.J.M. van Laarhoven and E.H.L. Aarts,Simulated Annealing. Theory and Applications (Reidel, Dordrecht, 1987).

    Google Scholar 

  • R. van Vliet and H. Cardon, Combining a graph partitioning and a TSP neural network to solve the MTSP, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (North-Holland, Amsterdam, 1991).

    Google Scholar 

  • P. Vanbommel, T. Vanderweide and C.B. Lucasius, Genetic algorithms for optimal logical database design, Information and Software Technology 36 (1994) 725.

    Article  Google Scholar 

  • J. Vancza and A. Markus, Genetic algorithms in process planning, Computers in Industry 17 (1991) 181.

    Article  Google Scholar 

  • P. Vanhentenryck, H. Simonis and M. Dincbas, Constraint satisfaction using constraint logic programming, Artificial Intelligence 58 (1992) 113.

    Article  Google Scholar 

  • H. Vanhove and A. Verschoren, Genetic algorithms and trees 1. Recognition trees (the fixed-width case), Computers and Artificial Intelligence 13 (1994) 453.

    Google Scholar 

  • M.M. Vanhulle and G.A. Orban, Representation and processing in a stochastic neural network. An integrated approach, Neural Networks 4 (1991) 643.

    Article  Google Scholar 

  • M. Vaz-Pato and C.L. Martins, Heuristic approaches for the feeder bus network design problem, Working paper, Instituto Superior de Economica e Gestao, Universidade Técnica de Lisboa, Portugal (1995).

    Google Scholar 

  • V.S. Vempati, C.L. Chen and S.F. Bullington, An effective heuristic for flow-shop problems with total flow time as criterion, Computers and Industrial Engineering 25 (1993) 219.

    Article  Google Scholar 

  • R. Vemuri and R. Vemuri, Genetic algorithm for MCM partitioning, Electronics Letters 30 (1994a) 1270.

    Article  Google Scholar 

  • R. Vemuri and R. Vemuri, MCM layer assignment using genetic search, Electronics Letters 30 (1994b) 1635.

    Article  Google Scholar 

  • G. Venkataraman and G. Athithan, Spin-glass, the traveling salesman problem, neural networks and all that, Pramana — Journal of Physics 36 (1991) 1.

    Google Scholar 

  • V. Venugopal and T.T. Narendran, A genetic algorithm approach to the machine grouping problem with multiple objectives, Computers and Industrial Engineering 22 (1992a) 469.

    Article  Google Scholar 

  • V. Venugopal and T.T. Narendran, Cell-formation in manufacturing systems through simulated annealing. An experimental evaluation, European Journal of Operational Research 63 (1992b) 409.

    Article  Google Scholar 

  • M.G.A. Verhoeven, Parallel local search, Ph.D. Dissertation, Eindhoven University of Technology, The Netherlands (1996).

    Google Scholar 

  • M.G.A. Verhoeven and E.H.L. Aarts, Parallel local search, Journal of Heuristics 1 (1995) 43.

    Google Scholar 

  • M.G.A. Verhoeven, E.H.L. Aarts and P.C.J. Swinkels, A parallel 2-opt algorithm for the traveling-salesman problem, Future Generation Computer Systems 11 (1995) 175.

    Article  Google Scholar 

  • F.J. Vico and F. Sandoval, Use of genetic algorithms in neural networks definition,Lecture Notes in Computer Science 540 (1991) p. 196.

    Google Scholar 

  • R.V.V. Vidal,Applied Simulated Annealing, Lecture Notes in Economics and Mathematical Systems 396 (Springer, Berlin, 1993).

    Google Scholar 

  • G.A. Vignaux and Z. Michalewicz, A genetic algorithm for the linear transportation problem, IEEE Transactions of Systems, Man and Cybernetics 21 (1991) 445.

    Google Scholar 

  • H.-P. Voigt, Soft genetic operators in evolutionary algorithms,Lecture Notes in Computer Science 899 (1995) p. 123.

    Google Scholar 

  • G. von Laszewski and H. Mühlenbein, Partitioning a graph with a parallel genetic algorithm,Lecture Notes in Computer Science 496 (1991) p. 165.

    Google Scholar 

  • J.M. Voogd, P.M.A. Sloot and R. Vandantzig, Comparison of vector and parallel implementations of the simulated annealing algorithm, Future Generation Computer Systems 11 (1995) 467.

    Article  Google Scholar 

  • S. Voß, Dynamic tabu search strategies for the travelling purchaser problem, Annals of Operations Research (1996a) 253.

  • S. Voß, Observing logical interdependencies in tabu search: Methods and results, in:Modern Heuristic Search Methods, ed. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).

    Google Scholar 

  • S. Voß Solving quadratic assignment problems using the reverse elimination method, in:The Impact of Emerging Technologies on Computer Science and Operations Research, ed. S.G. Nash and A. Sofer,Operations Research/Computer Science Interfaces 4 (Kluwer, Boston, 1995).

    Google Scholar 

  • S. Voß, Tabu search. Applications and prospects, in:Network Optimization Problems, ed. D.-Z. Du and P.M. Pardalos (World Scientific, Singapore, 1993a).

    Google Scholar 

  • S. Voß, The two stage hybrid flowshop scheduling problem with sequence dependent setup times, in:Operations Research in Production Planning and Control, ed. G. Fandel, T. Gulledge and A. Jones (Springer, Berlin, 1993b).

    Google Scholar 

  • S. Voß, Concepts for parallel tabu search, in:Symposium on applied Mathematical Programming and Modelling, ed. I. Maros (Budapest, 1993c) p. 595.

  • S. Voß, Network design formulations in schedule synchronization,Lecture Notes in Economics and Mathematical Systems 386 (1992) p. 137.

    Google Scholar 

  • I. Wacholder, J. Han and R.C. Mann, A neural network algorithm for the multiple traveling salesman problem, in:Proceedings of the IEEE Annual International Conference on Neural Networks, San Diego (1988) p. 305.

  • G.A. Walters and D.K. Smith, Evolutionary design algorithm for optimal layout of tree networks, Engineering Optimization 24 (1995) 261.

    Google Scholar 

  • G.A. Walters and T. Lohbeck, Optimal layout of tree networks using genetic algorithms, Engineering Optimization 22 (1993) 27.

    Google Scholar 

  • J. Wang, Neural network models and their applications, Journal of Industrial Technology 11 (1995), forthcoming.

  • J. Wang, A deterministic annealing neural network for convex programming, Neural Networks 7 (1994a) 629.

    Article  Google Scholar 

  • J. Wang, Artificial neural networks versus natural neural networks. A connectionist paradigm for preference assessment, Decision Support Systems 11 (1994b) 415.

    Article  Google Scholar 

  • J. Wang, Multiple-objective optimization of machining operations based on neural networks, International Journal of Advanced Manufacturing Technology 8 (1993a) 235.

    Article  Google Scholar 

  • J. Wang, Analysis and design of a recurrent neural network for linear programming, IEEE Transactions on Circuits and Systems I. Fundamental Theory and Applications 40 (1993b) 613.

    Article  Google Scholar 

  • J. Wang, Analog neural network for solving the assignment problem, Electronics Letters 28 (1992a) 1047.

    Google Scholar 

  • J. Wang, Recurrent neural networks for solving quadratic programming problems with equality constraints, Electronics Letters 28 (1992b) 1345.

    Google Scholar 

  • J. Wang, On the asymptotic properties of recurrent neural networks for optimization, International Journal of Pattern Recognition and Artificial Intelligence 5 (1991) 581.

    Article  Google Scholar 

  • J. Wang and V. Chankong, Recurrent neural networks for linear programming. Analysis and design principles, Computers and Operations Research 19 (1992) 297.

    Article  Google Scholar 

  • J. Wang and V. Chankong, Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute acceptance sampling inspection plans, Journal of Intelligent Manufacturing 2 (1991) 327.

    Article  Google Scholar 

  • J. Wang and H. Li, Solving simultaneous linear equations using recurrent neural networks, Information Sciences 76 (1993) 255.

    Article  MathSciNet  Google Scholar 

  • J. Wang and B. Malakooti, Characterization of training errors in supervised learning using gradient-based learning rules, Neural Networks 6 (1993) 1073.

    Google Scholar 

  • J. Wang and B. Malakooti, A feedforward and neural network for multiple criteria decision making, Computers and Operations Research 19 (1992) 151.

    Article  Google Scholar 

  • J. Wang and Y. Takefuji,Neural Networks in Design and Manufacturing (World Scientific, Singapore, 1993).

    Google Scholar 

  • J. Wang, J. Yang and H. Lee, Multicriteria order acceptance decision support in over-demanded job shops. A neural network approach, Mathematical and Computer Modeling 19 (1994) 1.

    Article  Google Scholar 

  • J. Wang, J. Yang and V.B. Gargeya, Tool requirement planning in stochastic job shops. A simulated annealing approach, Computers and Industrial Engineering 24 (1993) 249.

    Article  Google Scholar 

  • Q. Wang, X. Sun, B.L. Golden and J. Jia, Using artificial neural networks to solve the orienteering problem, Annals of Operations Research 61 (1995) 111–120.

    Article  Google Scholar 

  • S.H. Wang and N.P. Archer, A neural-network technique in modeling multiple criteria multiple person decision-making, Computers and Operations Research 21 (1994) 127.

    Article  Google Scholar 

  • X.D. Wang and T. Chen, Performance and area optimization of VLSI systems using genetic algorithms, VLSI Design 3 (1995) 43.

    Google Scholar 

  • G.S. Wasserman and A. Sudjianto, All subsets regression using a genetic search algorithm, Computers and Industrial Engineering 27 (1994) 489.

    Article  Google Scholar 

  • T. Watanabe, Y. Hashimoto, I. Nishikawa and H. Tokumaru, Line balancing using a genetic evolution model, Control Engineering Practice 3 (1995) 69.

    Article  Google Scholar 

  • C. Wen-Chyuan, G.J. Gutierrez and P. Kouvelis, Simulated annealing and tabu search, in:Intelligent Design and Manufacturing, ed. A. Kusiak (Wiley, Chichester, 1992).

    Google Scholar 

  • F. Werner, On the heuristic solution of the permutation flow shop problem by path algorithms, Computers and Operations Research 20 (1993) 707.

    Article  Google Scholar 

  • D. Whitley, A review of models for simple and cellular genetic algorithms in:Applications of Modern Heuristic Methods, ed. V. Rayward-Smith (Alfred Waller, Henley-on-Thames, 1995).

  • D. Whitley, A genetic algorithm tutorial, Statistics and Computing 4 (1994) 65.

    Article  Google Scholar 

  • D. Whitley, The GENITOR algorithm and selection pressure. Why raking based allocation of reproductive trials is best, in:Proceedings of the Third International Conference on Genetic Algorithms, ed. J.D Shaffer (Morgan Kaufmann, San Mateo, 1989) p. 116.

    Google Scholar 

  • D. Whitley, R. Beveridge, C. Graves and K. Mathias, Test driving three 1995 genetic algorithms. New test functions and geometric matching, Journal of Heuristics 1 (1995) 67.

    Google Scholar 

  • D. Whitley, S. Dominic, R. Das and C.W. Anderson, Genetic reinforcement learning for neurocontrol problems, in:Genetic Algorithms for Machine Learning, ed. J.J. Grefenstette (Kluwer, Boston, 1994).

    Google Scholar 

  • D. Whitley and J. Kanth, GENITOR. A different genetic algorithm, in:Proceedings of the Rocky Mountain Conference on Artificial Intelligence, Denver (1988) p. 118.

  • D. Whitley and D.J. Schaffer,International Workshop on Combinations of Genetic Algorithms and Neural Networks (IEEE Computer Society Press, Los Alamitos, California, 1992).

    Google Scholar 

  • D. Whitley and T. Starkweather, GENITOR II. A distributed genetic algorithm, Journal of Experimental and Theoretical Artificial Intelligence 2 (1990) 189.

    Google Scholar 

  • D. Whitley, T. Starkweather and C. Bogart, Genetic algorithms and neural networks. Optimizing connections and connectivity, Computing 14 (1990) 347.

    Google Scholar 

  • D. Whitley, T. Starkweather and D. Fuquay, Scheduling problems and the traveling salesman. The genetic edge recombination operator, in:Proceedings of the 3rd International Conference on Genetic Algorithms, ed. J.D. Schaffer (Morgan Kaufmann, San Mateo, 1989) p. 133.

    Google Scholar 

  • M. Widmer, Job shop scheduling with tooling constraints. A tabu search approach, Journal of the Operational Research Society 42 (1991) 75.

    Google Scholar 

  • M. Widmer and A. Hertz, A new heuristic method for the flow shop sequencing problem, European Journal of Operational Research 41 (1989) 186.

    Article  Google Scholar 

  • M.R. Wilhelm and T.I. Ward, Solving quadratic assignment problems by simulated annealing, IIE Transactions 19 (1987) 107.

    Google Scholar 

  • T.M. Willems and J.E. Rooda, Neural networks for job-shop scheduling, Control Engineering Practice 2 (1994) 31.

    Article  Google Scholar 

  • R.J. Willis and B.J. Terrill, Scheduling the Australian state cricket season using simulated annealing, Journal of the Operational Research Society 45 (1994) 276.

    Google Scholar 

  • D.J. Willshaw and C. von der Malsburg, A marker induction mechanism for the establishment of ordered neural mappings. Its application to the retinotectal problem, Philosophical Transactions of the Royal Society, Series B 287 (1979) 203.

    Google Scholar 

  • G. Wilson and C. Pawley, On the stability of the TSP algorithm of Hopfield and Tank, Biological Cybernetics 58 (1988) 63.

    Article  PubMed  Google Scholar 

  • J.M. Wilson, A genetic algorithm for the generalized assignment problem, Working paper, Business School, Loughborough University, England (1995).

    Google Scholar 

  • R.I. Wilson, Ranking college football teams. A neural-network approach, Interfaces 25 (1995) 44.

    Google Scholar 

  • R. Wilson and R. Sharda, Bankruptcy prediction using neural networks, Decision Support Systems 11 (1994) 545.

    Article  Google Scholar 

  • S.S. Wilson, Teaching network connectivity using simulated annealing on a massively parallel processor, Proceedings of the IEEE 79 (1991) 559.

    Article  Google Scholar 

  • E.E. Witte, R.D. Chamberlain and M.A. Franklina, Parallel simulated annealing using speculative computation, IEEE Transactions on Parallel and Distributed Systems 2 (1991) 483.

    Article  Google Scholar 

  • E. Wong, Stochastic neural networks, Algorithmica 6 (1991) 466.

    Article  Google Scholar 

  • K.P. Wong and C.C. Fung, Simulated annealing based economic dispatch algorithm, IEEE Proceedings — C Generation Transmission and Distribution 140 (1993) 509.

    Google Scholar 

  • K.P. Wong and Y.W. Wong, Short term hydrothermal scheduling 2. Parallel simulated annealing approach, IEE Proceedings — Generation Transmission and Distribution 141 (1994) 502.

    Article  Google Scholar 

  • W.S. Wong, Matrix representation and gradient flows for NP-hard problems, Journal of Optimization Theory and Applications 87 (1995) 197.

    Google Scholar 

  • D.L. Woodruff, Chunking applied to reactive tabu search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • D.L. Woodruff, Ghost image processing for minimum covariance determinants, ORSA Journal on Computing 7 (1995) 463.

    Google Scholar 

  • D.L. Woodruff, Simulated annealing and tabu search. Lessons from a line search, Computers and Operations Research 21 (1994) 823.

    Article  MathSciNet  Google Scholar 

  • D.L. Woodruff, Subcontracting when there are setups, deadlines, and tooling cost, in:Proceedings of the Intelligent Scheduling Systems Symposium, ed. W.T. Scherer and D.E. Brown, San Francisco, CA (1992) p. 337.

  • D.L. Woodruff and D.M. Rocke, Heuristic search algorithms for the minimum ellipsoid, American Statistical Association, Institute of Mathematical Statistics and Interface Foundation of North America 2 (1992) 69.

    Google Scholar 

  • D.L. Woodruff and D.M. Rocke, Robust multivariate location and shape in high dimension using compound estimators, Journal of the American Statistical Association 89 (1994) 888.

    MathSciNet  Google Scholar 

  • D.L. Woodruff and E. Zemel, Hashing vectors for tabu search, Annals of Operations Research 41 (1993) 123.

    Article  Google Scholar 

  • A. Wren and D.O. Wren, A genetic algorithm for public transport driver scheduling, Computers and Operations Research 22 (1995) 101.

    Article  Google Scholar 

  • M.B. Wright, Timetabling county cricket fixtures using a form of tabu search, Journal of the Operational Research Society 45 (1994) 758.

    Google Scholar 

  • M.B. Wright, A fair allocation of county cricket opponents, Journal of the Operational Research Society 43 (1992) 195.

    Google Scholar 

  • M.B. Wright and R.C. Marett, A preliminary investigation into the performance of heuristic search methods applied to compound combinatorial problems, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • S.J. Wu and P.T. Chow, Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization, Engineering Optimization 24 (1995a) 137.

    Google Scholar 

  • S.J. Wu and P.T. Chow, Steady-state genetic algorithms for discrete optimization of trusses, Computers and Structures 56 (1995b) 979.

    Article  Google Scholar 

  • Y. Wu and R.L. Wainwright, Near-optimal triangulations of a point set using genetic algorithms, in:Proceedings of the 7th Symposium on Artificial Intelligence, Stillwater (1993) p. 122.

  • B.J. Wythoff, Backpropagation neural networks. A tutorial, Chemometrics and Intelligent Laboratory Systems 18 (1993) 115.

    Article  Google Scholar 

  • Q. Xia and S. Macchietto, Routing, scheduling and product mix optimization by minimax algebra models, Chemical Engineering Research and Design 72 (1994) 408.

    Google Scholar 

  • Y.S. Xia and J.S. Wang, Neural-network for solving linear-programming problems with bounded variables, IEEE Transactions on Neural Networks 6 (1995) 515.

    Article  Google Scholar 

  • Y. Xin, Simulated annealing with extended neighborhood, International Journal of Computer Mathematics 40 (1991) 169.

    Google Scholar 

  • D. Xu and I. Kumazawa, Single minimum method for combinatorial optimization problems and its application to the TSP problem, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E76A (1993) 742.

    Google Scholar 

  • J. Xu, List of interesting optimization codes in public domain, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).

    Google Scholar 

  • J. Xu and J.P. Kelly, A robust network flow based tabu search approach for the vehicle routing problem, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).

    Google Scholar 

  • L. Xu and E. Oja, Improved simulated annealing, Boltzmann machine, and attributed graph matching,Lecture Notes in Computer Science 412 (1990) p. 151.

    Google Scholar 

  • X. Xu and W.T. Tsai, Effective neural algorithms for the traveling salesman problem, Neural Networks 4 (1991) 193.

    Article  Google Scholar 

  • M. Yagiura and T. Ibaraki, Genetic and local search algorithms as robust and simple optimization tools in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • T. Yamada and R. Nakano, Job-shop scheduling by simulated annealing combined with deterministic local search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • Y. Yamada, E. Tomita and H. Takahashi, A randomized algorithm for finding a near maximum clique and its experimental evaluations, Systems and Computers in Japan 25 (1994) 1.

    MathSciNet  Google Scholar 

  • A. Yamamoto, M. Ohta, H. Ueda, A. Ogihara and K. Fukunaga, Asymmetric neural network and its application to knapsack problem, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E78A (1995) 300.

    Google Scholar 

  • K.K. Yang, L.C. Tay and C.C. Sum, A comparison of stochastic scheduling rules for maximizing project net present, European Journal of Operational Research 85 (1995) 327.

    Article  Google Scholar 

  • T.Y. Yang, Z.S. He and K.K. Cho, An effective heuristic method for generalized job-shop scheduling with due-dates, Computers and Industrial Engineering 26 (1994) 647.

    Article  Google Scholar 

  • X.F. Yang and M. Gen, Evolution program for bicriteria transportation problem, Computers and Industrial Engineering 27 (1994) 481.

    Article  Google Scholar 

  • M. Yannakakis, The analysis of local search problems and their heuristics,Lecture Notes in Computer Science 415 (1990) p. 298.

    Google Scholar 

  • X. Yao, Call routing by simulated annealing, International Journal of Electronics 79 (1995) 379.

    Google Scholar 

  • X. Yao, A review of evolutionary artificial neural networks, International Journal of Intelligent Systems 8 (1993a) 539.

    Google Scholar 

  • X. Yao, An empirical study of genetic operators in genetic algorithms, Microprocessing and Microprogramming 38 (1993b) 707.

    Article  Google Scholar 

  • X. Yao, Finding approximate solutions to NP-hard problems by neural networks is hard, Information Procession Letters 41 (1992) 93.

    Article  Google Scholar 

  • C.W. Yeh, C.K. Cheng and T.T.Y. Lin, Optimization by iterative improvement. An experimental evaluation on 2-way partitioning, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 14 (1995) 145.

    Article  Google Scholar 

  • I.C. Yeh, Construction site layout using annealed neural-network, Journal of Computing in Civil Engineering 9 (1995) 201.

    Article  Google Scholar 

  • P.P.C. Yip and Y.H. Pao, Combinatorial optimization with use of guided evolutionary simulated annealing, IEEE Transactions on Neural Networks 6 (1995) 290.

    Article  Google Scholar 

  • P.P.C. Yip and Y.H. Pao, A guided evolutionary simulated annealing approach to the quadratic assignment problem, IEEE Transactions on Systems, Man and Cybernetics 24 (1994) 1383.

    Google Scholar 

  • L. Yong, K. Lishan and D.J. Evans, The annealing evolution algorithm as function optimizer, Parallel Computing 21 (1995) 389.

    Article  Google Scholar 

  • B.J. Yoon, D.J. Holmes, G. Langholz and A. Kandel, Efficient genetic algorithms for training layered feedforward neural systems, Information Sciences 76 (1994) 67.

    Article  Google Scholar 

  • Y.O. Yoon, G. Swales and T.M. Margavio, A comparison of discriminant-analysis versus artificial neural networks, Journal of the Operational Research Society 44 (1993) 51.

    Google Scholar 

  • R.A. Young and A. Reel, A hybrid genetic algorithm for a logic problem, in:Proceedings of the 9th European Conference on Artificial Intelligence, ed. L.C. Aiello (Pitman, London, 1990) p. 744.

    Google Scholar 

  • A.L. Yuille, Generalized deformable models, statistical physics and matching problems, Neural Comptutation 2 (1990) 1.

    Google Scholar 

  • J.M. Yunker and J.D. Tew, Simulation optimization by genetic search, Mathematics and Computers in Simulation 37 (1994) 17.

    Article  MathSciNet  Google Scholar 

  • D. Yuret, From genetic algorithms to efficient optimization, M.Sc. Dissertation, Department of Electrical Engineering and Computer Science, M.I.T., Cambridge, MA (1994).

    Google Scholar 

  • D. Yuret and M. de la Maza, Dynamic hill climbing. Overcoming the limitations of optimization techniques, in:Proceedings of the 2nd Turkish Symposium on Artificial Intelligence and Artificial Neural Networks (1993) p. 254.

  • D. Yuval,Genetic Algorithms and Robotics. A Heuristic Strategy for Optimization (World Scientific, Singapore, 1991).

    Google Scholar 

  • M. Zachariasen and M. Dam, Tabu search on the geometric traveling salesman problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).

    Google Scholar 

  • S. Zanakis, J. Evans and A. Vazacopoulos, Heuristic methods and applications: A categorized survey, European Journal of Operational Research 43 (1989) 88.

    Article  Google Scholar 

  • M.R. Zargham, A simulated annealing multilayer router, Integration — The VLSI Journal 13 (1992) 179.

    Article  Google Scholar 

  • S.H. Zegordi, K. Itoh and T. Enkawa, Knowledgable simulated annealing scheme for the early tardy flow-shop scheduling problem, International Journal of Production Research 33 (1995a) 1449.

    Google Scholar 

  • S.H. Zegordi, K. Itoh and T. Enkawa, Minimizing makespan for flow shop scheduling by combining simulated annealing with sequencing knowledge, European Journal of Operational Research 85 (1995b) 515.

    Article  Google Scholar 

  • S.H. Zegordi, K. Itoh, T. Enkawa and S.L. Chung, Simulated annealing scheme incorporating move desirability table for solution of facility layout problems, Journal of the Operations Research Society of Japan 38 (1995) 1.

    MathSciNet  Google Scholar 

  • S.W. Zhang, X. Zhu and L.H. Zou, Second order neural nets for constrained optimization, IEEE Transactions on Neural Networks 3 (1992) 1021.

    Article  Google Scholar 

  • D. Zhu and R. Padman, Neural networks for heuristics selection. An application in resource-constrained project scheduling, in:The Impact of Emerging Technologies on Computer Science and Operations Research, ed. S.G. Nash and A. Sofer,Operations Research/Computer Science Interfaces 4 (Kluwer, Boston, 1995).

    Google Scholar 

  • V. Zissimopoulos, On the performance guarantee of neural networks for NP-hard optimization problems, Information Processing Letters 54 (1995) 317.

    Article  Google Scholar 

  • P.J. Zwietering, E.H. Aarts and J. Wessels, Exact classification with two-layered perceptions, International Journal of Neural Systems 3 (1992) 143.

    Article  Google Scholar 

  • P.J. Zwietering, E.H.L. Aarts and J. Wessels, The design and complexity of exact multilayered perceptions, International Journal of Neural Systems 2 (1991) 185.

    Article  Google Scholar 

  • P.J. Zwietering, M.J.A.L. Van Kraaij, E.H.L. Aarts and J. Wessels, Neural networks and production planning, in:Proceedings of 4th International Conference on Neural Networks and their Applications, Nîmes, France (1991) p. 529.

    Google Scholar 

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Osman, I.H., Laporte, G. Metaheuristics: A bibliography. Ann Oper Res 63, 511–623 (1996). https://doi.org/10.1007/BF02125421

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