Metaheuristics: A bibliography
 Ibrahim H. Osman,
 Gilbert Laporte
 … show all 2 hide
Purchase on Springer.com
$39.95 / €34.95 / £29.95*
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
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; nonmonotonic search strategies; spacesearch methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.
 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.
 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.
 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. CrossRef
 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.
 E.H.L. Aarts and H.P. Stehouwer, Neural networks and the travelling salesman problem, Working paper, Eindhoven University of Technology, The Netherlands (1993).
 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).
 E.H.L. Aarts and J.H.M. Korst, Boltzmann machines for travelling salesman problems, European Journal of Operational Research 39 (1989b) 79. CrossRef
 E.H.L. Aarts and J.H.M. Korst, Boltzmann machines as a model for parallel annealing, Algorithmica 6 (1991) 437. CrossRef
 E.H.L. Aarts and J.K. Lenstra,Local Search in Combinatorial Optimization (Wiley, Chichester, 1996), forthcoming.
 E.H.L. Aarts and P.J.M. van Laarhoven, Local search in coding theory, Discrete Mathematics 106 (1992) 11. CrossRef
 H. Abada and E. ElDarzi, A metaheuristic for the timetabling problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 W.A.T.W. Abdullah, Seeking global minima, Journal of Computational Physics 110 (1994) 320. CrossRef
 S. Abe, J. Kawakami and K. Hirasawa, Solving inequality constrained combinatorial optimization problems by the Hopfield neural networks, Neural Networks 5 (1992) 663.
 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).
 D.A. Abramson, A very highspeed architecture for simulated annealing, Computer 25 (1992) 27. CrossRef
 D.A. Abramson, Constructing school timetables using simulated annealing. Sequential and parallel algorithms, Management Science 37 (1991) 98.
 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).
 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.
 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).
 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.
 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.
 B. Adensodiaz, Restricted neighborhood in the tabu search for the flowshop problem, European Journal of Operational Research 62 (1992) 27. CrossRef
 I. Ahmad and M.K. Dhodhi, Task assignment using a problemspace genetic algorithm, Concurrency Practice and Experience 7 (1995a) 411.
 I. Ahmad and M.K. Dhodhi, On themway graph partitioning problem, Computer Journal 38 (1995b) 237.
 R.H. Ahmadi and C.S. Tang, An operation partitioning problem for automated assembly system design, Operations Research 39 (1991) 824.
 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. CrossRef
 A.N. Aizawa and B.W. Wah, A sequential sampling procedure for genetic algorithms, Computers and Mathematics with Applications 27 (1994) 77. CrossRef
 A.S. AlMahmeed, Tabu search, combination and integration, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 K.S. AlSultan, A tabu search approach to the clustering problem, Pattern Recognition 28 (1995) 1443. CrossRef
 K.S. AlSultan and S.Z. Selim, A global algorithm for the fuzzy clustering problem, Pattern Recognition 26 (1993) 1357. CrossRef
 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. CrossRef
 A.S. Alfa, S.S. Heragu and M.Y. Chen, A 3opt based simulated annealing algorithm for vehicle routing problems, Computers and Industrial Engineering 21 (1991) 635. CrossRef
 J.R.A. Allwright and D.B. Carpenter, A distributed implementation of simulated annealing for the travelling salesman problem, Parallel Computing 10 (1989) 335. CrossRef
 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. CrossRef
 C.J. Alpert and A.B. Kahng, Recent directions in netlist partitioning. A survey, Integration — The VLSI Journal 19 (1995) 1.
 I. Althofer and K.U. Koschnick, On the convergence of threshold accepting, Applied Mathematics and Optimization 24 (1991) 183. CrossRef
 R. AlvarezValdes, 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).
 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. CrossRef
 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.
 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.
 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).
 S. Amellal and B. Kaminska, Functional synthesis of digitalsystems with TASS, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 13 (1994) 537. CrossRef
 M.M. Amini and M. Racer, A rigorous computational comparison of alternative solution methods for the generalized assignment problem, Management Science 40 (1994) 868.
 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. CrossRef
 C.A. Anderson, K.F. Jones and J. Ryan, A twodimensional genetic algorithm for the Ising problem, Complex Systems 5 (1991) 327.
 E.J. Anderson and M.C. Ferris, Genetic algorithms for combinatorial optimization. The assembly line balancing problem, ORSA Journal on Computing 6 (1994) 161.
 I.P. Androulakis and V. Venkatasubramanian, A genetic algorithmic framework for process design and optimization, Computers and Chemical Engineering 15 (1991) 217. CrossRef
 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.
 B. Angeniol, G. de la CroisVanbois and J. le Texier, Selforganizing feature maps and the TSP, Neural Networks 1 (1988) 289. CrossRef
 B. Angeniol, The neural networks market. A commercial survey, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (NorthHolland, Amsterdam, 1991).
 S. Anily and A. Federgruen, Simulated annealing methods with general acceptance probabilities, Journal of Applied Probability 24 (1987) 657.
 N. Ansari, R. Sarasa and G.S. Wang, An efficient annealing algorithm for global optimization in Boltzmann machines, Applied Intelligence 3 (1993) 177. CrossRef
 P. Antognetti and V. Milutinovic,Neural Networks. Concepts, Applications, and Implementations (PrenticeHall, Englewood Cliffs, 1991).
 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. CrossRef
 J. Arabas, A genetic approach to the Hopfield neuralnetwork in the optimization problems, Bulletin of the Polish Academy of Sciences — Chemistry 42 (1994) 59.
 S. Areibi and A. Vannelli, Circuit partitioning using a tabu search approach, IEEE International Symposium on Circuits and Systems 3 (1993) 1643.
 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.
 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. CrossRef
 S. Arunkumar and T. Chockalingham, Genetic search algorithms and their randomized operators, Computers and Mathematics with Applications 25 (1993) 91. CrossRef
 J.B. Atkinson, A greedy lookahead heuristic for combinatorial optimization. Application to vehicle scheduling with time, Journal of the Operational Research Society 45 (1994) 673.
 G. Ausiello and M. Protasi, Local search, reducibility and approximability of NPoptimization problems, Information Processing Letters 54 (1995) 73. CrossRef
 R. Azencott, Simulated annealing, Asterisque 161 (1988) 223.
 R. Azencott, Simulated Annealing. Parallelization Techniques (Wiley, Chichester, 1992).
 G.P. Babu and M.N. Murty, A near optimal initial seed value selection inKmeans algorithm using a genetic algorithm, Pattern Recognition Letters 14 (1993) 763. CrossRef
 G.P. Babu and M.N. Murty, Simulated annealing for selecting optimal initial seeds in thekmeans algorithm, Indian Journal of Pure and Applied Mathematics 25 (1994) 85.
 F.Q. Bac and V.I. Perov, New evolutionary genetic algorithms for NPcomplete combinatorial optimization problems, Biological Cybernetics 69 (1993) 229. CrossRef
 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.
 T. Bäck, The interaction of mutation rate, selection and selfadaptation within a genetic algorithm, in:Parallel Problem Solving from Nature, PPSN II Proceedings, ed. R. Männer and B. Manderick (NorthHolland, Amsterdam, 1992).
 T. Bäck and F. Hoffmeister, Basic aspects of evolution strategies, Statistics and Computing 4 (1994) 65. CrossRef
 T. Bäck and H.P. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation 1 (1993) 1.
 A. Bahrami and C.H. Dagli, Hybrid intelligent packing system (HIPS) through integration of artificial neural networks, artificialintelligence, and mathematicalprogramming, Applied Intelligence 4 (1994) 321. CrossRef
 F. Baiardi and S. Orlando, Strategies for a massively parallel implementation of simulated annealing, in:Lecture Notes in Computer Science 366 (1989) p. 273.
 J. Balakrishnan and P.D. Jog, Manufacturing cellformation using similarity coefficients and a parallel genetic TSP algorithm. Formulation and comparison, Mathematical and Computer Modelling 21 (1995) 61. CrossRef
 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 withKmeans clustering, Psychometrika 59 (1994) 509.
 P.V. Balakrishnan and V.S. Jacob, Genetic algorithms for product design, Working paper WP939015, College of Business, The Ohio State University (1993).
 S. Baluja, Structure and performance of finegrain parallelism in genetic search, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 155.
 S.H. Bang, O.T.C. Chen, J.C.F. Chang and B.J. Sheu, Paralleled hardware annealing in multilevel Hopfield neural networks for optimalsolutions, IEEE Transactions on Circuits and Systems II — Analog and Digital Signal Processing 42 (1995) 46. CrossRef
 W. Banzhaf and F.H. Eckman,Evolution and Biocomputation. Computational Models of Evolution, Lecture Notes in Computer Science 899 (Springer, Berlin, 1995).
 V.C. Barbosa, A distributed implementation of simulated annealing, Journal of Parallel and Distributed Computing 6 (1989) 411. CrossRef
 V.C. Barbosa and M.C.S. Boeres, An OCCAMbased evaluation of a parallel version of simulated annealing, Microprocessing and Microprogramming 30 (1990) 85. CrossRef
 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. CrossRef
 J.F. Bard and T.A. Feo, An algorithm for the manufacturing equipment selection problem, IIE Transactions 23 (1991) 83.
 J.F. Bard and T.A. Feo, Operations sequencing in discrete parts manufacturing, Management Science 35 (1989) 249.
 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).
 J.W. Barnes and J.B. Chambers, Solving the jobshop scheduling problem with tabu search, IIE Transactions 27 (1995) 257.
 J.W. Barnes and M. Laguna, A tabu search experience in production scheduling, Annals of Operations Research 41 (1993) 141. CrossRef
 J.W. Barnes and M. Laguna, Solving the multiplemachine weighted flow time problem using tabu search, IIE Transactions 25 (1993) 121.
 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.
 D. Barrios, J.A.P. Ruydiaz, J. Rios and J. Segovia, Conditions for convergence of genetic algorithms through Walshseries, Computers and Artificial Intelligence 13 (1994) 441.
 E.B. Bartlett, A stochastic training algorithm for artificial neural networks, Neurocomputing 6 (1994) 31. CrossRef
 R. Battiti, Ractive search. Toward selftuning heuristics, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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.
 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).
 R. Battiti and G. Tecchiolli, Local search with memory. Benchmarking RTS, OR Spektrum 17 (1995a) 67. CrossRef
 R. Battiti and G. Tecchiolli, Training neural nets with the reactive tabu, IEEE Transactions on Neural Networks 6 (1995b) 1185. CrossRef
 R. Battiti and G. Tecchiolli, The reactive tabu, ORSA Journal on Computing 6 (1994) 126.
 R. Battiti and G. Tecchiolli, Parallel biased search for combinatorial optimization. Genetic algorithms and tabu, Microprocessors and Microsystems 16 (1992) 351. CrossRef
 D.L. Battle and M.D. Vose, Isomorphisms of genetic algorithms, Artificial Intelligence 60 (1993) 155. CrossRef
 R.J. Bauer,Genetic Algorithms and Investment Strategies (Wiley, Chichester, 1994).
 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).
 J.C. Bean, Genetic algorithms and random keys for sequencing and optimization, ORSA Journal on Computing 6 (1994) 154.
 D. Beasley, D.R. Bull and R.R. Martin, An overview of genetic algorithms 1. Fundamentals, University Computing 15 (1993a) 58.
 D. Beasley, D.R. Bull and R.R. Martin, An overview of genetic algorithms 2. Research topics, University Computing 15 (1993b) 170.
 J.E. Beasley, ORlibrary. Distributed test problems by electronic mail, Journal of the Operational Research Society 41 (1990) 1069 (ftp site address: mscmga.ms.ic.ac.uk).
 J.E. Beasley and F. Goffinet, A delaunay triangulationbased heuristic for the Euclidean Steiner problem, Networks 24 (1994) 215.
 J.E. Beasley and P.C. Chu, A genetic algorithm for the set covering problem, Working Paper, The Management School, Imperial College, London (1994).
 R.K. Belew and L.B. Booker,Proceedings of the Fourth International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1991).
 C.J.P. Bélisle, Convergence theorems for a class of simulated annealing algorithms on R(D), Journal of Applied Probability 29 (1992) 885.
 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. CrossRef
 H.F. Beltran and D. SkorinKapov, On minimumcost isolated failure immune networks, Telecommunication Systems 3 (1994) 183. CrossRef
 M. Benaim and L. Tomasini, Competitive and selforganizing algorithms based on the minimization of an information criterion, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (NorthHolland, Amsterdam, 1991).
 D. Benarieh and O. Maimon, Annealing method for PCB assembly scheduling on 2 sequentialmachines, International Journal of Computer Integrated Manufacturing 5 (1992) 361.
 M. Bengtsson and Roivainen, Using the PottsGlass for solving the clustering problem, International Journal of Neural Systems 6 (1995) 119. CrossRef
 W.A. Bennage and A.K. Dhingra, Single and multiobjective structural optimization in discretecontinuous variables using simulated annealing, International Journal for Numerical Methods in Engineering 38 (1995) 2753. CrossRef
 M.S.T. Benten and S.M. Sait, Genetic scheduling of task graphs, International Journal of Electronics 77 (1994) 401.
 P.J. Bentley, The evolution of solid object designs using genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 A. Bertoni and M. Dorigo, Implicit parallelism in genetic algorithms, Artificial Intelligence 61 (1993) 307. CrossRef
 D.J. Bertsimas and J. Tsitsiklis, Simulated annealing, Statistical Science 8 (1993) 10.
 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.
 D. Bhandari and S.K. Pal, Directed mutation in genetic algorithms, Information Sciences 79 (1994) 251. CrossRef
 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. CrossRef
 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. CrossRef
 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.
 J.E. Biegel and J.J. Davern, Genetic algorithms and job shop scheduling, Computers and Industrial Engineering 19 (1990) 81. CrossRef
 C. Bierwirth, A generalized permutation approach to job shop scheduling with genetic algorithms, OR Spektrum 17 (1995) 87. CrossRef
 J. Biethahn and V. Nissen,Evolutionary Algorithms in Management Applications (Springer, Berlin, 1995), forthcoming.
 G.L. Bilbro and W.E. Snyder, Optimization of functions with many minima, IEEE Transactions on Systems, Man and Cybernetics 21 (1991) 840.
 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. CrossRef
 J.A. Bland, A derivativefree exploratory tool for function minimisation based on tabu search, Advances in Engineering Software 19 (1994) 91. CrossRef
 J.A. Bland and G.P. Dawson, Largescale layout of facilities using a heuristic hybrid algorithm, Applied Mathematical Modelling 18 (1994) 500. CrossRef
 J.A. Bland and G.P. Dawson, Tabu search and design optimization, ComputerAided Design 23 (1991) 195. CrossRef
 J.J. Bland, Discretevariable optimal structural design using tabu search, Structural Optimization 10 (1995) 87. CrossRef
 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.
 J. Blazewicz and R. Walkowiak, A local search approach for 2dimensional irregular cutting, OR Spektrum 17 (1995) 93. CrossRef
 J. Blazewicz, P. Hawryluk and R. Walkowiak, Using a tabu search approach for solving the twodimensional irregular cutting problem, Annals of Operations Research 41 (1993) 313. CrossRef
 F.F. Boctor, A linear formulation of the machinepart cellformation problem, International Journal of Production Research 29 (1991) 343.
 K.D. Boese and A.B. Kahng, Bestsofar vs whereyouare. Implications for optimal finitetime annealing, Systems and Control Letters 22 (1994) 71. CrossRef
 N. Boissin and J.L. Lutton, A parallel simulated annealing algorithm, Parallel Computing 19 (1993) 859. CrossRef
 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. CrossRef
 E. Bonomi and J.L. Lutton, TheNcity travelling salesman problem and the metropolis algorithm, SIAM Review 26 (1984) 551. CrossRef
 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.
 N. Borin and P. Farris, A sensitivity analysis of retailer shelf management models, Journal of Retailing 71 (1995) 153. CrossRef
 J. Bos, Zoning in forest management. A quadratic assignment problem solved by simulated annealing, Journal of Environmental Management 37 (1993) 127. CrossRef
 S. Bose and A.R. Saha, Implementation of a heuristic method for standard cell placement, International Journal of Electronics 74 (1993) 281.
 J. Bovet, C. Constantin and D. de Werra, A convoy scheduling problem, Discrete Applied Mathematics 30 (1991) 1. CrossRef
 P. Brandimarte, Neighborhood searchbased optimization algorithms for production scheduling. A survey, Computer Integrated Manufacturing Systems 5 (1992) 167. CrossRef
 P. Brandimarte, Routing and scheduling in a flexible job shop by tabu search, Annals of Operations Research 41 (1993) 157. CrossRef
 P. Brandimarte and M. Calderini, A hierarchical bicriterion approach to integrated process plan selection and jobshop scheduling, International Journal of Production Research 33 (1995) 161.
 H. Brasel, T. Tautenhahn and F. Werner, Constructive heuristic algorithms for the open shop problem, Computing 51 (1993) 95.
 H. Braun, On solving travelling salesman problems by genetic algorithms,Lecture Notes in Computer science 496 (1991) 129.
 C. Brind, C. Muller and P. Prosser, Stochastic techniques for resource management, BT Technology Journal 13 (1995) 55.
 S.P. Brooks, A hybrid optimization algorithm, Applied Statistics — Journal of the Royal Statistical Society Series C44 (1995) 530.
 S.P. Brooks and B.J.T. Morgan, Optimization using simulated annealing, Statistician 44 (1995) 241.
 S.P. Brooks and B.J.T. Morgan, Automatic starting point selection for function optimization, Statistics and Computing 4 (1994) 173. CrossRef
 D.E. Brown and C.L. Huntley, A practical application of simulated annealing to clustering, Pattern Recognition 25 (1992) 401. CrossRef
 J. Bruck and J. Goodman, On the power of neural networks for solving hard problems, Journal of Complexity 6 (1990) 127. CrossRef
 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).
 R. Brunelli, Training neural nets through stochastic minimization, Neural Networks 7 (1994) 1405. CrossRef
 R. Brunelli, Optimal histogram partitioning using a simulated annealing technique, Pattern Recognition Letters 13 (1992) 581. CrossRef
 M.J. Brusco and L.W. Jacobs, Costanalysis of alternative formulations for personnel scheduling in continuously operating organizations, European Journal of Operational Research 86 (1995) 249. CrossRef
 M.J. Brusco and L.W. Jacobs, A simulated annealing approach to the cyclic staffscheduling problem, Naval Research Logistics 40 (1993a) 69.
 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.
 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.
 B.P. Buckles and F.E. Petry,Genetic Algorithms (IEEE Computer Society Press, Los Alamitos, California, 1992).
 R.S. Bucy and R.S. Diesposti, Decision tree design by simulated annealing, RAIRO — Mathematical Modelling and Numerical Analysis 27 (1993) 515.
 J. Buhman and H. Kuhnel, Complexity optimized data clustering by competitive neural networks, Neural Computation 5 (1993) 75.
 T. Bultan and C. Aykanat, Circuit partitioning using meanfield annealing, Neurocomputing 8 (1995) 171. CrossRef
 R.E. Burkard and E. Cela, Heuristics for biquadratic assignment problems and their computational comparison, European Journal of Operational Research 83 (1995) 283. CrossRef
 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. CrossRef
 L.I. Burke and J.P. Ignizio, Neural networks and operationsresearch. An overview, Computers and Operations Research 19 (1992) 179. CrossRef
 A. Burns, N. Hayes and M.F. Richardson, Generating feasible cyclic schedules, Control Engineering Practice 3 (1995) 151. CrossRef
 J. Cagan, Shape annealing solution to the constrained geometric knapsack problem, ComputerAided Design 26 (1994) 763. CrossRef
 J. Cagan and W.J. Mitchell, Optimally directed shape generation by shape annealing, Environment and Planning B — Planning and Design 20 (1993) 5.
 A. Cangelosi, D. Parisi and S. Nolfi, Celldivision and migration in a genotype for neural networks, NetworkComputation in Neural Systems 5 (1994) 497. CrossRef
 B.Y Cao and G. Uebe, Solving transportation problems with nonlinear side constraints with tabu search, Computers and Operations Research 22 (1995) 593. CrossRef
 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. CrossRef
 S.E. Carlson, R. Shonkwiler and M.E. Ingrim, Comparison of 3 nonderivative optimization methods with a genetic algorithm for component selection, Journal of Engineering Design 5 (1994) 367.
 G.A. Carpenter and S. Grossberg, A massively parallel architecture for a selforganizing neural network, Computer Vision, Graphics and Image Processing 37 (1987) 54. CrossRef
 H.M. Cartwright and B. Jesson, The analysis of waste flow data from multiunit industrial complexes using genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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. CrossRef
 A. Casotto, F. Romeo and A. SangiovanniVincentelli, A parallel simulated annealing algorithm for the placement of macrocells, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 6 (1987) 838. CrossRef
 D.J. Castelino, S. Hurley and N.M. Stephens, A tabu search algorithm for frequency assignment, Annals of Operations Research 63 (1996) 301.
 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).
 O. Catoni, Exponential triangular cooling schedules for simulated annealing algorithms. A casestudy,Lecture Notes in Control and Information Sciences 177 (1992a) p. 74.
 O. Catoni, Rough large deviation estimates for simulated annealing. Application to exponential schedules, Annals of Probability 20 (1992b) 1109.
 S. Cavalieri, A. Distefano and O. Mirabella, Optimal path determination in a graph by Hopfield neuralnetworks, Neural Networks 7 (1994) 397. CrossRef
 V. Cerny, A thermodynamical approach to the travelling salesman problem. An efficient simulated annealing algorithm, Journal of Optimization Theory and Applications 45 (1985) 41. CrossRef
 M. Cesare, J.C. Santamarina, C.J. Turkstra and E. Vanmarcke, Riskbased bridge management. Optimisation and inspection scheduling, Canadian Journal of Civil Engineering 21 (1994) 897.
 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.
 J. Chakrapani and J. SkorinKapov, Connection machine implementation of a tabu search algorithm for the traveling salesman problem, Journal of Computing and Information Technology 1 (1993a) 29.
 J. Chakrapani and J. SkorinKapov, Massively parallel tabu search for the quadratic assignment problem, Annals of Operations Research 41 (1993b) 327. CrossRef
 J. Chakrapani and J. SkorinKapov, 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).
 J. Chakrapani and J. SkorinKapov, A connectionist approach to the quadratic assignment problem, Computers and Operations Research 19 (1992) 287. CrossRef
 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. CrossRef
 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. CrossRef
 K.C. Chan and H. Tansri, A study of genetic crossover operations on the facilities layout problem, Computers and Industrial Engineering 26 (1994) 537. CrossRef
 W.T. Chan, T.F. Fwa and C.Y. Tan, Roadmaintenance planning using genetic algorithms 1. Formulation, Journal of Transportation Engineering — ASCE 120 (1994) 693.
 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.
 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. CrossRef
 R. Chandrasekharam, V.V. Vinod and S. Subramanian, Genetic algorithm for test scheduling with different objectives, Integration — The VLSI Journal 17 (1994b) 153. CrossRef
 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.
 R.I. Chang and P.Y. Hsiao, Solving system partitioning problem using a massively parallel biocomputing network, IFIP Transactions A — Computer Science and Technology 51 (1994) 129.
 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.
 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.
 P. Chardaire, Location of concentrators using simulated annealing, in:Applications of Modern Heuristics Methods, ed. V.J. RaywardSmith (Alfred Waller, HenleyonThames, 1995).
 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. CrossRef
 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.
 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).
 I. Charon and O. Hudry, The noising method. A new method for combinatorial optimization, Operations Research Letters 14 (1993) 133. CrossRef
 K.M. Cheh, Goldberg J.B. and R.G. Askin, A note on the effect of neighborhoodstructure in simulated annealing, Computers and Operations Research 18 (1991) 537. CrossRef
 C. Chen, F. Swift and R. Racine, A computer application in apparel manufacturing management, Computers and Industrial Engineering 23 (1992) 439. CrossRef
 C.L. Chen, N.A. Cotruvo and W. Baek, A simulated annealing solution to the cellformation problem, International Journal of Production Research 33 (1995) 2601.
 C.L. Chen, V.S. Vempati and N. Aljaber, An application of genetic algorithms for flowshop problems, European Journal of Operational Research 80 (1995) 389. CrossRef
 H.C. Chen, Machine learning for informationretrieval. Neural networks, symbolic learning and genetic algorithms, Journal of the American Society for Information Science 46 (1995) 194. CrossRef
 L.N. Chen and K. Aihara, Chaotic simulated annealing by a neuralnetwork model with transient chaos, Neural Networks 8 (1995) 915. CrossRef
 S.K. Chen, P. Mangiameli and D. West, The comparative ability of selforganizing neural networks to define cluster structure, Omega 23 (1995) 271. CrossRef
 W.H. Chen and B. Srivastava, Simulated annealing procedures for forming machine cells in group technology, European Journal of Operational Research 75 (1994) 100. CrossRef
 Y.L. Chen and C.C. Liu, Optimal multiobjective var planning using an interactive satisfying method, IEEE Transactions on Power Systems 10 (1995) 664. CrossRef
 B. Cheng and D.M. Titterington, Neural networks. A review from a statistical perspective, Statistical Science 9 (1994) 2.
 R.W. Cheng, M. Gen and M. Sasaki, Filmcopy deliverer problem using genetic algorithms, Computers and Industrial Engineering 29 (1995) 549. CrossRef
 R.W. Cheng and M. Gen, Crossover on intensive search and traveling salesman problem, Computers and Industrial Engineering 27 (1994) 485. CrossRef
 M. Chester,Neural Networks. A Tutorial (PrenticeHall, Englewood Cliffs, 1993).
 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.
 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. CrossRef
 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. CrossRef
 T. Chockalingam and S. Arunkumar, Genetic algorithmbased heuristics for the mapping problem, Computers and Operations Research 22 (1995) 55. CrossRef
 T. Chockalingam and S. Arunkumar, A randomized heuristic for the mapping problem. The genetic approach, Parallel Computing 18 (1992) 1157. CrossRef
 C.J. Chou, C.C. Liu and Y.T. Hsiao, A multiobjective optimization approach to loading balance and grounding planning in 3phase 4wire distributionsystems, Electric Power Systems Research 31 (1994) 163. CrossRef
 M. Christoph and K.H. Hoffmann, Scaling behavior of optimal simulated annealing schedules, Journal of Physics A — Mathematical and General 26 (1993) 3267. CrossRef
 C.H. Chu and D. Widjaja, Neural network system for forecasting method selection, Decision Support Systems 12 (1994) 13. CrossRef
 P.C. Chu and J.E. Beasley, A genetic algorithm for the generalized assignment problem, Working paper, The Management School, Imperial College, London (1995).
 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.
 CHARME, Bull. CEDIAG, Louveciennes, France (1993).
 CHIP, Reference Manual, COSYTEC, Parc Club OrsayUniversité, Orsay, France (1993).
 A. Cichocki and R. Unbehauen,Neural Networks for Optimization and Signal Processing (Wiley, New York, 1993).
 S.E. Cieniawski, J.W. Eheart and S. Ranjithan, Using genetic algorithms to solve a multiobjective groundwater monitoring problem, Water Resources Research 31 (1995) 399. CrossRef
 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.
 J.P. Cohoon, S.U. Hegde, W.N. Martin and D.S. Richards, Distributed genetic algorithms for the floorplan design problem, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 10 (1991) 483. CrossRef
 J.P. Cohoon, W.N. Martin and D. Richards, A multipopulation genetic algorithm for solving theKpartition problem on hypercubes, in:Proceedings of the 4th International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo 1991) p. 244.
 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.
 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.
 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 (NorthHolland, Amsterdam 1992a).
 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).
 A. Colorni, M. Dorigo and V. Maniezzo, Genetic algorithms and highly constrained problems. The timetable case,Lecture Notes in Computer Science 496 (1991a) p. 55.
 A. Colorni, M. Dorigo and V. Maniezzo, Positive feedback as a search strategy, Working paper 9116, Department of Electronics, Politecnico di Milano, Italy (1991b).
 M. Conlon, The controlled random search procedure for function optimization, Communications in StatisticsSimulation and Computation 21 (1992) 919.
 D.T. Connolly, General purpose simulated annealing, Journal of the Operational Research Society 43 (1992) 495.
 D.T. Connolly, An improved annealing schedule for the QAP, European Journal of Operational Research 46 (1990) 93. CrossRef
 D.G. Conway and M.A. Venkataramanan, Genetic search and the dynamic facility layout problem, Computers and Operations Research 21 (1994) 955. CrossRef
 J.S. Cook and B.T. Han, Efficient heuristics for robot acquisition planning for a CIM system, OR Spektrum 17 (1995) 99. CrossRef
 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.
 J.F. Cordeau, M. Gendreau and G. Laporte, A tabu search heuristic for periodic and multidepot vehicle routing problems, Working paper CRT9575, 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.
 D. Costa, A tabu search algorithm for computing an operational timetable, European Journal of Operational Research 76 (1994) 98. CrossRef
 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.
 P. Courrieu, A convergent generator of neural networks, Networks 6 (1993) 835.
 I.B. Crabtree, Resource scheduling. Comparing simulated annealing with constraint programming, BT Technology Journal 13 (1995) 121.
 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).
 T.G. Crainic, M. Gendreau, P. Soriano and M. Toulouse, A tabu search procedure for multicommodity locationallocation with balancing requirements, Annals of Operations Research 41 (1993) 359. CrossRef
 T.G. Crainic, M. Toulouse and M. Gendreau, Parallel asynchronous tabu search for multicommodity locationallocation with balancing requirements, Annals of Operations Research 63 (1996) 277.
 T.G. Crainic, M. Toulouse and M. Gendreau, Synchronous tabu search parallelization strategies for multicomodity locationallocation with balancing requirements, OR Spektrum 17 (1995) 113. CrossRef
 H. Crockett, Applications of neural networks in finance,Proceedings of the ASIS Annual Meeting 31 (1994) p. 105.
 A.E. Croker and V. Dhar, A knowledge representation for constraint satisfaction problems, IEEE Transactions on Knowledge and Data Engineering 5 (1993) 740. CrossRef
 D. Cubanski and D. Cyganski, Multivariate classification through adaptive Delaunaybased C0 spline approximation, IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 403. CrossRef
 M. Cuppini, A genetic algorithm for channel assignment problems, European Transactions on Telecommunications and Related Technologies 5 (1994) 285.
 F. Curatelli, Implementation and evaluation of genetic algorithms for system partitioning, International Journal of Electronics 78 (1995) 435.
 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.
 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.
 R. Cuykendall and R. Reese, Scaling the neural TSP algorithm, Biological Cybernetics 60 (1989) 365. CrossRef
 C.H. Dagli and S. Sittisathanchai, Genetic neuroscheduler for job shop scheduling, Computers and Industrial Engineering 25 (1993) 267. CrossRef
 B. Dahlin and O. Sallnas, Harvest scheduling under adjacency constraints. A case study from the Swedish subalpine region, Scandinavian Journal of Forest Research 8 (1993) 281.
 F. Dammeyer, P. Forst and S. Voß, On the cancellation sequence method of tabu search, ORSA Journal on Computing 3 (1991) 262.
 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. CrossRef
 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. CrossRef
 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.
 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. CrossRef
 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).
 L. Davis,Handbook of Genetic Algorithms (Van Nostrand Reinhold, New York, 1991).
 L. Davis,Genetic Algorithms and Simulated Annealing (Pitman, London, 1987).
 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.
 M. de la Maza and D. Yuret, Dynamic hill climbing, AI Expert 9 (1994) 26.
 D. de Werra and A. Hertz, Tabu search techniques. A tutorial and an application to neural networks, OR Spektrum 11 (1989) 131. CrossRef
 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.
 G.J. Deboeck,The Trading Edge. Neural, Genetic and Fuzzy Systems for Chaotic and Financial Markets (Wiley, Chichester, 1994).
 R. Dechter, Constraint networks, in:Encyclopedia of Artifical Intelligence, Vol. 1, ed. S.C. Shaprio (Wiley, Chichester, 1992).
 R. Dechter, Enhancement schemes for constraint satisfaction processing. Backjumping, learning and cutset decomposition, Artificial Intelligence 41 (1990) 273. CrossRef
 R. Dechter and I. Meiri, Experimental evaluation of preprocessing algorithms for constraint satisfaction problems, Artificial Intelligence 68 (1994) 211. CrossRef
 R. Dechter and J. Pearl, Network based heuristics for the constraint satisfaction problems, Artifical Intelligence 34 (1988) 1. CrossRef
 A. Degloria, P. Faraboschi and M. Olivieri, Block placement with a Boltzmann machine, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 13 (1994) 694. CrossRef
 A. Degloria, P. Faraboschi and M. Olivieri, Clustered Boltzmann machines. Massively parallel architectures for constrained optimization problems, Parallel Computing 19 (1993a) 163. CrossRef
 A. Degloria, P. Faraboschi and M. Olivieri, Design of a massively parallel SIMD architecture for the Boltzmann machine, Microprocessing and Microprogramming 37 (1993b) 153. CrossRef
 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.
 C. Degroot, D. Wurtz and K.H Hoffmann, Simulated annealing and evolution strategy. A comparison, Helvetica Physica Acta 63 (1990) 843.
 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.
 K.A. DeJong and W.M. Spears, An analysis of the interacting roles of populationsize and crossover in genetic algorithms,Lecture Notes in Computer Science 496 (1991) p. 38.
 K.A. DeJong and W.M. Spears, Using genetic algorithms to solve NPcomplete problems, in:Proceedings of the 3rd International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1989) p. 124.
 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).
 A. Dekkers and E.H.L. Aarts, Global optimization and simulated annealing, Mathematical Programming 50 (1991) 367. CrossRef
 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).
 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).
 M. Dell'Amico and M. Trubian, Applying tabu search to the jobshop scheduling problem, Annals of Operations Research 41 (1993) 231. CrossRef
 F.D. DellaCroce, R. Tadei, and G. Volta, A genetic algorithm for the job shop problem, Computers and Operations Research 22 (1995) 15. CrossRef
 F.D. DellaCroce, R. Tadei and R. Rolando, Solving a realworld project scheduling problem with a genetic approach, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 65.
 P.J. Denning, Genetic algorithms, American Scientist 80 (1992) 12.
 W.S. Desarbo, R.L. Oliver and A. Rangaswamy, A simulated annealing methodology for clusterwise linearregression, Psychometrika 54 (1989) 707.
 A.K. Dhingra and W.A. Bennage, Discrete and continuous variable structural optimization using tabu search, Engineering Optimization 24 (1995) 177.
 M.K. Dhodhi, F.H. Hielscher, R.H. Storer and J. Bhasker, Datapath synthesis using a problemspace genetic algorithm, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 14 (1995) 934. CrossRef
 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.
 P. Dige, C. Lund and H.F. Ravn, Timetabling by simulated annealing,Lecture Notes in Economics and Mathematical Systems 396 (1993) p. 1151.
 H. Ding, A.A. Elkeib and R. Smith, Optimal clustering of power networks using genetic algorithms, Electric Power Systems Research 30 (1994) 209. CrossRef
 N. Dodd, Slow annealing versus multiple fast annealing runs. An empirical investigation, Parallel Computing 16 (1990) 269. CrossRef
 W.B. Dolan, P.T. Cummings and M.D. Levan, Algorithmic efficiency of simulated annealing for heatexchanger network design, Computers and Chemical Engineering 14 (1990) 1039. CrossRef
 W. Domschke, Schedule synchronization for public transit networks, OR Spektrum 11 (1989) 17. CrossRef
 W. Domschke, P. Forst and S. Voß, Tabu search techniques for the quadratic semiassignment problem, in:New Directions for Operations Research in Manufacturing, ed. G. Fandel, T. Gulledge and A. Jones (Springer, Berlin, 1992).
 W. Domschke and G. Krispin, Location and layout planning. A survey, Working Paper, Institut für Betriebswirtschaftlehre, Fachgebiet Operations Research, Technische Hochschule Darmstadt, Germany (1996).
 M. Dorigo, Using transputers to increase speed and flexibility of genetics based machine learning systems, Microprocessing and Microprogamming 34 (1992) 147. CrossRef
 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).
 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 shopfloor disturbances by fuzzyreasoning, International Journal of HumanComputer Studies 42 (1995) 287. CrossRef
 U. Dorndorf and E. Pesch, Evolutionbased learning in a job shop scheduling environment, Computers and Operations Research 22 (1995) 25. CrossRef
 U. Dorndorf and E. Pesch, Fast clustering algorithms, ORSA Journal on Computing 6 (1994) 141.
 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).
 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.
 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).
 K.A. Dowsland, Simulated annealing solutions for multiobjective scheduling and timetabling, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 K.A. Dowsland, Some experiments with simulated annealing techniques for packing problems, European Journal of Operational Research 68 (1993) 389. CrossRef
 K.A. Dowsland, Hillclimbing, simulated annealing and the Steiner problem in graphs, Engineering Optimization 17 (1991) 91.
 K.A. Dowsland, A timetabling problem in which clashes are inevitable, Journal of the Operational Research Society 41 (1990) 907.
 A. Drexl, A simulated annealing approach to the multiconstraint zeroone knapsack problem, Computing 40 (1988) 1.
 A. Drexl and K. Haase, Sequential analysis based randomized regret methods for lot sizing and scheduling, Journal of Operational Research Society 47 (1996) 251.
 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).
 N. Dubois and D. de Werra, EPCOT. An efficient procedure for colouring optimally with tabu search, Computers and Mathematics with Applications 25 (1993) 35. CrossRef
 E.J. Dubuc, Bandwidth reduction by simulated annealing, International Journal for Numerical Methods in Engineering 37 (1994) 3977. CrossRef
 G. Dueck and T. Scheuer, Threshold accepting. A general purpose optimization algorithm appearing superior to simulated annealing, Journal of Computational Physics 90 (1990) 161. CrossRef
 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 AIAITR149, Artificial Intelligence Applications Institute, University of Edinburgh (1994a).
 T. Duncan, Intelligent vehicle scheduling. Experiences with a constraint based approach, Working paper AIAITR150, 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. CrossRef
 M.D. Durand, Parallel simulated annealing. Accuracy vs speed in placement, IEEE Design and Test of Computers 6 (1989) 8.
 R. Durbin, R. Szeliski and A. Yuille, An analysis of the elastic net approach to the TSP, Neural Computation 2 (1990) 348.
 R. Durbin, R. Szeliski and A. Yuille, An analysis of the elastic net approach to the travelling salesman problem, Neural Computation 1 (1989) 348.
 R. Durbin and D. Willshaw, An analogue approach to the TSP using an elastic net method, Nature 326 (1987) 689. CrossRef
 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.
 M. Efe, Statisticalanalysis of parallel randomized algorithms for VLSI placement and implementation on workstation networks, Microprocessors and Microsystems 19 (1995) 341. CrossRef
 R.W. Eglese, Routing winter gritting vehicles, Discrete Applied Mathematics 48 (1994) 231. CrossRef
 R.W. Eglese, Simulated annealing. A tool for operational research, European Journal of Operational Research 46 (1990) 271. CrossRef
 R.W. Eglese, Heuristics in operational research, in:Recent Developments in Operational Research, ed. V. Belton and B. O'Keefe (Pergamon Press, Oxford, 1986).
 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).
 A.E. Eiben, E.H.L. Aarts and K.M. van Hee, Global convergence of genetic algorithms. A Markovchain analysis,Lecture Notes in Computer Science 496 (1991) p. 4.
 S.S. Erenguc and H. Pirkul, Heuristic, genetic and tabu search. Foreword, Computers and Operations Research 21 (1994) 799. CrossRef
 E. Erwin, K. Obermayer and K. Schulten, Convergence properties of selforganizing maps, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (NorthHolland, Amsterdam, 1991).
 R. Ettelaie and M.A. Moore, Zerotemperature scaling and simulated annealing, Journal de Physique 48 (1987) 1255.
 U. Faigle and W. Kern, On the convergence of simulated annealing algorithms, SIAM Journal on Control and Optimization 29 (1991) 153. CrossRef
 U. Faigle and B. Schrader, On the convergence of stationary distributions in simulated annealing algorithms, Information Processing Letters 27 (1988) 189. CrossRef
 U. Faigle and W. Kern, Some convergence results for probabilistic tabu search, ORSA Journal on Computing 4 (1992) 32.
 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.
 E. Falkenauer, A new representation and operators for GAs applied to grouping problems, Evolutionary Computation 2 (1994) 123.
 E. Falkenauer, The grouping genetic algorithms. Widening the scope of the GAs, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 79.
 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.
 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.
 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.
 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 (McGrawHill, New Delhi, 1993) p. 375.
 H.L. Fang, P. Ross and D. Corne, A promising genetic algorithm approach to jobshop scheduling, rescheduling and openshop scheduling problems, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 375.
 L.V. Fausett, Fundamentals of Neural Networks. Architectures, Algorithms, and Applications (PrenticeHall, Englewood Cliffs, 1994).
 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. CrossRef
 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 computeraided process planning, Journal of Manufacturing Systems 8 (1989) 17.
 T.A. Feo and J.F. Bard, Flight scheduling and maintenance base planning, Management Science 35 (1989) 1415.
 T.A. Feo and M.G.C. Resende, Greedy randomized adaptive search procedures, Journal of Global Optimization 6 (1995) 109. CrossRef
 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.
 T.A. Feo and M.G.C. Resende, A probabilistic heuristic for a computationally difficult set covering problem, Operations Research Letters 8 (1989) 67. CrossRef
 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. CrossRef
 M. Ferri and M. Piccioni, Optimal selection of statistical units. An approach via simulated annealing, Computational Statistics and Data Analysis 13 (1992) 47. CrossRef
 C.N. Fiechter, A parallel tabu search algorithm for large traveling salesman problems, Discrete Applied Mathematics 51 (1994) 243. CrossRef
 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.
 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. CrossRef
 M.M. Fischer and S. Gopal, Artificial neural networks. A new approach to modeling interregional telecommunication flows, Journal of Regional Science 34 (1994) 503.
 S.T. Fischer, A note on the complexity of local search problems, Information Processing Letters 53 (1995) 69. CrossRef
 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).
 C. Fleurent and J.A. Ferland, Genetic and hybrid algorithms for graph coloring, Annals of Operations Research 63 (1996) 437.
 M. Flynn, Some computer organizations and their effectiveness, IEEE Transactions on Computers 21 (1972) 948.
 T.C. Fogarty,Evolutionary Computing, AISB Workshop, Lecture Notes in Computer Science 993 (Springer, Berlin, 1995).
 T.C. Fogarty,Evolutionary Computing Proceedings, Lecture Notes in Computer Science 865 (Springer, Berlin, 1994).
 D.B. Fogel, A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation 64 (1995) 397.
 D.B. Fogel, An introduction to simulated evolutionary optimization, IEEE Transactions on Neural Networks 5 (1994a) 3. CrossRef
 D.B. Fogel, Asymptotic convergence properties of genetic algorithms and evolutionary programming. Analysis and experiments, Cybernetics and Systems 25 (1994b) 389.
 D.B. Fogel, Applying evolutionary programming to selected traveling salesman problems, Cybernetics and Systems 24 (1993) 27.
 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.
 G.B. Fogel and D.B. Fogel, Continous evolutionary programming. Analysis and experiments, Cybernetics and Systems 26 (1995) 79.
 D.B. Fogel and L.C. Stayton, On the effectiveness of crossover in simulated evolutionary optimization, Biosystems 32 (1994) 171. CrossRef
 L.J. Fogel, A.J. Owens and M.J. Walsh,Artificial Intelligence Through Simulated Evolution (Wiley, New York 1966).
 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.
 S.Y. Foo, Y. Takefuji and H. Szu, Job shop scheduling based on modified TankHopfield linear programming networks, Engineering Applications of Artificial Intelligence 7 (1994) 321. CrossRef
 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.
 S. Forrest,Proceedings of an International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1993).
 S. Forrest and M. Mitchell, What makes a problem hard for a genetic algorithm? Some results and their explanation, Machine Learning 13 (1993) 285. CrossRef
 J.C. Fort, Solving a combinatorial problem via selforganizing process. An application of the Kohonen algorithm to the traveling salesman problem, Biological Cybernetics 59 (1988) 33. CrossRef
 P.H. Fortemps, A job shop scheduling with set up time, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 103.
 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. CrossRef
 B.L. Fox, Faster simulated annealing, SIAM Journal on Optimization 5 (1995) 488. CrossRef
 B.L. Fox, Random restarting versus simulated annealing, Computers and Mathematics with Applications 27 (1994) 33. CrossRef
 B.L. Fox, Integrating and accelerating tabu search, simulated annealing, and genetic algorithms, Annals of Operations Research 41 (1993) 47. CrossRef
 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).
 M.S. Fox, Constraint directed search. A case study of job shop scheduling, Working paper CMURITR8322, The Robotics Institute, CarnegieMellon University (1983).
 M.S. Fox and S.F. Smith, ISIS. A knowledgebased system for factury scheduling, Expert Systems 1 (1984) 25.
 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.
 P.M. França, M. Gendreau, G. Laporte and F.M. Müller, Themtraveling salesman problem with minimax objective, Transportation Science 29 (1995) 267.
 J.A. Freeman,Exploring Neural Networks with Mathematics (AddisonWesley, Wokingham, England, 1994).
 J.A. Freeman and D.M. Skapura,Neural Networks. Algorithms, Applications and Programming Techniques (AddisonWesley, Wokingham, England, 1991).
 E.C. Freuder and R.J. Wallace, Partial constraint satisfaction, Artificial Intelligence 58 (1992) 21. CrossRef
 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. CrossRef
 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.
 T.L. Friesz, G. Anandalingam, N.J. Mehta, K. Nam, S.J. Shah and R.L. Tobin, The multiobjective equilibrium network design problem revisited. A simulated annealing approach, European Journal of Operational Research 65 (1993) 44. CrossRef
 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.
 D. Frost and R. Dechter, Deadend 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.
 R. Gangadharan and C. Rajendran, A simulated annealing heuristic for scheduling in a flowshop with bicriteria, Computers and Industrial Engineering 27 (1994) 473. CrossRef
 B.I. Garcia, J.Y. Potvin and J.M. Rousseau, A parallel implementation of the tabu search heuristic for vehiclerouting problems with time window constraints, Computers and Operations Research 21 (1994) 1025. CrossRef
 M.R. Garey and D.S. Johnson,Computers and Intractability. A Guide to the Theory of NPCompleteness (Freemann, New York, 1979).
 A.H. Gee and R.W. Prager, Limitations of neural networks for solving traveling salesman problems, IEEE Transactions on Neural Networks 6 (1995) 280. CrossRef
 E. Gelenbe,Neural Networks. Advances and Applications (NorthHolland, Amsterdam, 1991).
 S.B. Gelfand and S.K. Mitter, Simulated annealing type algorithms for multivariate optimization, Algorithmica 6 (1991a) 419. CrossRef
 S.B. Gelfand and S.K. Mitter, Weak convergence of Markovchain sampling methods and annealing algorithms to diffusions, Journal of Optimization Theory and Applications 68 (1991b) 483. CrossRef
 D.D. Gemmill, Solution to the assortment problem via the genetic algorithm, Mathematical and Computer Modelling 16 (1992) 89. CrossRef
 M. Gendreau, A. Hertz and G. Laporte, A tabu search heuristic for the vehicle routing problem, Management Science 40 (1994) 1276.
 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.
 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. CrossRef
 J.A. George, J.M. George and B.W. Lamar, Packing differentsized circles into a rectangular container, European Journal of Operational Research 84 (1995) 693. CrossRef
 H. Ghaziri, Solving routing problems by a selforganizing feature map, in:Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula and J. Kangas (NorthHolland, Amsterdam, 1991).
 H. Ghaziri, Supervision in the selforganizing feature map: Application to the vehicle routing problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 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. CrossRef
 J.C. Gilkinson, L.C. Rabelo and B.O. Bush, A realworld scheduling problem using genetic algorithm, Computers and Industrial Engineering 29 (1995) 177. CrossRef
 G.C. Gini and C. Rogialli, CONSTRUCTOR. A constraintbased language, Computer Systems Science and Engineering 9 (1994) 255.
 R.S. Ginsberg, Dynamic backtracking, Journal of Artificial Intelligence Research 1 (1993) 25.
 L. Gislen, C. Peterson and B. Soderberg, Teachers and classes with neural networks, International Journal of Neural Systems 1 (1989) 167. CrossRef
 C.A. Glass, C.N. Potts and P. Shade, Unrelated parallel machine scheduling using local search, Mathematical and Computer Modelling 20 (1994) 41. CrossRef
 C.A. Glass and C.N. Potts, A comparison of local search methods for flow shop scheduling, Annals of Operations Research 63 (1996) 489.
 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).
 F. Glover, Tabu thresholding. Improved search by nonmonotonic trajectories, ORSA Journal on Computing 7 (1995b) 426.
 F. Glover, Scatter search and starpaths. Beyond the genetic metaphor, OR Spektrum 17 (1995c) 125. CrossRef
 F. Glover, Optimization by ghost image processes in neural networks, Computers and Operations Research 21 (1994a) 801. CrossRef
 F. Glover, Tabu search for nonlinear and parametric optimization (with links to genetic algorithms), Discrete Applied Mathematics 49 (1994b) 231. CrossRef
 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).
 F. Glover, Genetic algorithms and scatter search. Unexpected potentials, Statistics and Computing 4 (1994d) 131.
 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).
 F. Glover, Artificial intelligence, heuristic frameworks and tabu search, Managerial and Decision Economics 11 (1990a) 365.
 F. Glover, Tabu search. A tutorial, Interfaces 20 (1990b) 74.
 F. Glover, Tabu search. Part II, ORSA Journal on Computing 2 (1990c) 4.
 F. Glover, Tabu search. Part I, ORSA Journal on Computing 1 (1989) 190.
 F. Glover, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research 13 (1986) 533. CrossRef
 F. Glover, Heuristics for integer programming using surrogate constraints, Decision Sciences 8 (1977) 156.
 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. CrossRef
 F. Glover, J.P. Kelly and M. Laguna, Genetic algorithms and tabu search. Hybrids for optimization, Computers and Operations Research 22 (1995) 111. CrossRef
 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).
 F. Glover, M. Lee and J. Ryan, Leastcost network topology design for a new service. An application of tabu search, Annals of Operations Research 33 (1991) 351. CrossRef
 F. Glover and M. Laguna, Tabu search, in:Modern Heuristic Techniques for Combinatorial Problems, ed. C.C. Reeves (Blackwell, Oxford, 1993).
 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.
 F. Glover, M. Laguna, E.D. Taillard and D. de Werra,Tabu Search, Annals of Operations Research 43 (Baltzer, Basel, 1993).
 F. Glover and C. McMillan, The general employee scheduling problem. An integration of MS and AI, Computers and Operations Research 13 (1986) 563. CrossRef
 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).
 F. Glover and E. Pesch, TSP ejection chain, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).
 F. Glover, E. Pesch and I.H. Osman, Efficient facility layout planning, Graduate School of Business, University of Colorado, Boulder (1995).
 F. Glover and J. SkorinKapov, Heuristic advances in optimization integrating tabu search, ejection chains and neural networks, Working paper, Graduate School of Business, University of Colorado, Boulder (1993).
 F. Glover, E.D. Taillard and D. de Werra, A user's guide to tabu search, Annals of Operations Research 41 (1993) 3. CrossRef
 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).
 W.L. Goffe, G.D. Ferrier and J. Rogers, Global optimization of statistical functions with simulated annealing, Journal of Econometrics 60 (1994) 65. CrossRef
 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. CrossRef
 D.E. Goldberg, A note on Boltzmann tournament selection for genetic algorithms and population oriented simulated annealing, Complex Systems 4 (1990) 445.
 D.E. Goldberg,Genetic Algorithms in Search, Optimization, and Machine Learning (AddisonWesley, Wokingham, England, 1989).
 D.E. Goldberg, K. Deb and J.H. Clark, Genetic algorithms, noise and the sizing of populations, Complex Systems 6 (1992) 333.
 B.L. Golden, Charting new directions in OR and CS, ORSA Journal on Computing 6 (1994) 107.
 B.L. Golden and C.C. Skiscim, Using simulated annealing to solve routing and locationproblems, Naval Research Logistics 33 (1986) 261.
 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).
 M. Goldstein, Selforganizing 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.
 M. GorgesSchleuter, Explicit parallelism of genetic algorithms through population structures,Lecture Notes in Computer Science 496 (1991) p. 150.
 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. CrossRef
 H. Greenberg, Computational testing. Why, how and how much, ORSA Journal on Computing 2 (1990) 7.
 J.W. Greene and K.J. Supowit, Simulated annealing without rejected moves, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 5 (1986) 221. CrossRef
 D.R. Greening, Parallel simulated annealing techniques, Physica D42 (1990) 293.
 R.N. Greenwell, J.E. Angus and M. Finck, Optimal mutation probability for genetic algorithms, Mathematical and Computer Modelling 21 (1995) 1. CrossRef
 J.J. Grefenstette,Genetic Algorithms for Machine Learning (Kluwer, Boston, 1994).
 J.J. Grefenstette, Genetic algorithms, IEEE ExpertIntelligent Systems and Their Applications 8 (1993) 5.
 J.J. Grefenstette, Incorporating problem specific knowledge into genetic algorithms, in:Genetic Algorithms and Simulated Annealing, ed. L. Davis (Pitman, London, 1987).
 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).
 J.J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 16 (1986) 122.
 J.J. Grefenstette,Proceedings of an International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1985).
 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).
 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).
 F. Gruau, Automatic definition of modular neural networks, Adaptive Behavior 3 (1994) 151.
 J. Gu, Local search for satisfiability (SAT) problem, IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 1108.
 J. Gu, Design of efficient local search algorithms,Lecture Notes in Artificial Intelligence 604 (1992) p. 651.
 J. Gu and X.F. Huang, Efficient local search with search space smoothing. A casestudy of the traveling salesman problem (TSP), IEEE Transactions on Systems, Man and Cybernetics 24 (1994) 728.
 J. Gu, X.F. Huang and B. Du, A quantitative solution to constraint satisfaction problem (CSP), New Generation Computing 13 (1994) 99.
 A. Guinet, Scheduling independent jobs on uniform parallel machines to minimize tardiness criteria, Journal of Intelligent Manufacturing 6 (1995) 95. CrossRef
 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. CrossRef
 A. Gupta and M.S. Lam, Estimating missing values using neural networks, Journal of the Operational Research Society 47 (1996) 229.
 D.K. Gupta, An enhancement scheme for constraint satisfaction problems (CSPS), International Journal of Computer Mathematics 47 (1993) 177.
 M.C. Gupta, Y.P. Gupta and A. Kumar, Minimizing flow time variance in a singlemachine system using genetic algorithms, European Journal of Operational Research 70 (1993) 289. CrossRef
 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. CrossRef
 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.
 J. Haddock and J. Mittenthal, Simulation optimization using simulated annealing, Computers and Industrial Engineering 22 (1992) 387. CrossRef
 B. Hajek and G. Sasaki, Simulated annealing. To cool or not?, Systems and Control Letters 12 (1989) 443. CrossRef
 B. Hajek, Cooling schedules for optimal annealing, Mathematics of Operations Research 13 (1988) 311.
 P. Hajela and C.Y. Lin, Genetic algorithms in optimization problems with discrete and integer design variables, Engineering Optimization 19 (1992) 309.
 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. CrossRef
 B.T. Han, G. Diehr and J.S. Cook, Multipletype, twodimensional bin packing problems. Applications and algorithms, Annals of Operations Research 50 (1994) 239. CrossRef
 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.
 S. Hanafi, A. Fréville and A. ElAbdellaoui, 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).
 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 oilexploration, European Journal of Operational Research 58 (1992) 202. CrossRef
 P. Hansen and B. Jaumard, Algorithms for the maximum satisfiability problem, Computing 44 (1990) 279.
 P. Hansen and K.W. Lih, Heuristic reliability optimization by tabu search, Annals of Operations Research 63 (1996) 321.
 O. Hansson and A. Mayer, Decisiontheoretic 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).
 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.
 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).
 J.K. Hao and R. Dorne, Study of genetic search for the frequency assignment problem,Lecture Notes in Computer Science (Springer, Berlin, 1995), forthcoming.
 B.L.M. Happel and J.M.J. Murre, Design and evolution of modular neuralnetwork architectures, Neural Networks 7 (1994) 985.
 X. Hardyao, Finding approximate solutions to NPhard problems by neural networks is hard, Information Processing Letters 41 (1992) 93. CrossRef
 D. Harel and M. Sardas, Randomized graph drawing with heavyduty preprocessing, Journal of Visual Languages and Computing 6 (1995) 233. CrossRef
 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. CrossRef
 J.P. Hart and A.W. Shogan, Semigreedy heuristics. An empirical study, Operations Research Letters 6 (1987) 107. CrossRef
 S.M. Hart and C.L.S. Chen, Simulated annealing and the mapping problem. A computational study, Computers and Operations Research 21 (1994) 455. CrossRef
 M. Hasan and I.H. Osman, Local search algorithms for the maximal planar layout problem, International Transactions in Operational Research 2 (1995) 89. CrossRef
 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.
 S. Haykin,Neural Networks. A Comprehensive Foundation (MacMillan, New York, 1994).
 P. Healy and R. Moll, A new extension of local search applied to the dialaride problem, European Journal of Operational Research 83 (1995) 83. CrossRef
 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. CrossRef
 J. Heistermann, Application of a genetic approach as an algorithm for neural networks,Lecture Notes in Computer Science 496 (1991) p. 297.
 B.J. Hellstrom and L.N. Kanal, Asymmetric meanfield neural networks for multiprocessor scheduling, Neural Networks 5 (1992) 671.
 S.S. Heragu, Modeling the machine layout problem, Computers and Industrial Engineering 19 (1990) 294. CrossRef
 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. CrossRef
 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.
 S.S. Heragu and B. Mazacioglu, Variations of the simulated annealing algorithm applied to the order picking problem, Working paper 3791295, Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York (1991).
 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).
 L. Hérault and J.J. Niez, Neural network and combinatorial optimization. A study of NPcomplete graph problems, in:Neural Networks. Advances and Applications, ed. E. Gelenbe (NorthHolland, Amsterdam, 1991).
 L. Hérault and J.J. Niez, Neural networks and graphKpartitioning, Complex Systems 3 (1989) 521.
 E. Herbert and K.A. Dowsland, A family of genetic algorithms for the pallet loading problem, Annals of Operations Research 63 (1996) 415.
 J.W. Hermann and C.Y. Lee, Solving a class scheduling problem with genetic algorithm, ORSA Journal on Computing 7 (1995) 443.
 A. Hertz, Finding a feasible course schedule using tabu search, Discrete Applied Mathematics 35 (1992) 255. CrossRef
 A. Hertz, Tabu search for large scale timetabling problems, European Journal of Operational Research 54 (1991) 39. CrossRef
 A. Hertz, B. Jaumard and M. Poggi de Aragao, Local optima topology for thekcoloring problem, Discrete Applied Mathematics 49 (1994) 257. CrossRef
 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.
 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.
 A. Hertz and D. de Werra, The tabu search metaheuristic. How we used it, Annals of Mathematics and Artificial Intelligence 1 (1990) 111. CrossRef
 A. Hertz and D. de Werra, Using tabu search techniques for graphcoloring, Computing 39 (1987) 345.
 J. Hertz, A. Krogh and R.G. Palmer,Introduction to the Theory of Neural Computation (AddisonWesley, Wokingham, England, 1991).
 T.M. Heskes, E.T.P. Slijpen and B. Kappen, Cooling schedules for learning in neural networks, Physical Review E47 (1993) 4457.
 J. Hesser, R. Männer and O. Stucky, On Steiner trees and genetic algorithms,Lecture Notes in Artificial Intelligence 565 (1991) p. 509.
 D.B. Hibbert, Genetic algorithms in chemistry, Chemometrics and Intelligent Laboratory Systems 19 (1993) 277. CrossRef
 T. Hill, L. Marquez, M. O'Connor and W. Remus, Artificial neuralnetwork models for forecasting and decision making, International Journal of Forecasting 10 (1994) 5. CrossRef
 K.S. Hindi, Solving the CLSP by a tabu search heuristic, Journal of the Operational Research Society 47 (1996) 151.
 K.S. Hindi, Solving the singleitem, capacitated dynamic lotsizing problem with startup and reservation costs by tabu search, Computers and Industrial Engineering 28 (1995) 701. CrossRef
 K.S. Hindi and E. Toczylowski, Detailed scheduling of batchproduction in a cell with parallel facilities and common renewable resources, Computers and Industrial Engineering 28 (1995) 839. CrossRef
 K.S. Hindi and Y.M. Hamam, Solving the part families problem in discreteparts manufacture by simulated annealing, Production Planning and Control 5 (1994) 160.
 D.T. Hiquebran, A.S. Alfa, J.A. Shapiro and D.H. Gittoes, A revised simulated annealing and cluster1st route2nd algorithm applied to the vehicle routing problem, Engineering Optimization 22 (1994) 77.
 C.A. Hjorring, The vehicle routing problem and local search metaheuristics, Ph.D. Thesis, Department of Engineering Science, The University of Auckland, NZ (1995).
 K.H. Hoffmann, M. Christoph and M. Hanf, Optimizing simulated annealing,Lecture Notes in Computer Science 496 (1991) p. 221.
 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. CrossRef
 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.
 F. Hoffmeister and T. Bäck, Genetic algorithms and evolution strategies. Similarities and differences,Lecture Notes in Computer Science 496 (1991) p. 455.
 J. Hofmann and C. Schiemangk, Placement heuristics for generation of FMS layouts,Lecture Notes in Control and Information Sciences 143 (1990) p. 780.
 J.H. Holland,Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, 1992a).
 J.H. Holland, Genetic algorithms, Scientific American 267 (1992b) 66.
 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.
 A. Homaifar, C.X. Qi and S.H. Lai, Constrained optimization via genetic algorithms, Simulation 62 (1994) 242.
 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.
 C.E. Hong and B.M. McMillin, Relaxing synchronization in distributed simulated annealing, IEEE Transactions on Parallel and Distributed Systems 6 (1995) 189. CrossRef
 G. Hong, M. Zuckermann, R. Harris and M. Grant, A fast algorithm for simulated annealing, Physica Scripta T38 (1991) 40.
 J.N. Hooker, Testing heuristics. We have it all wrong, Journal of Heuristics 1 (1995) 33.
 J.N. Hooker, Needed. An empirical science of algorithms, Operations Research 42 (1994) 201.
 J.N. Hooker and N.R. Natraj, Solving general routing and scheduling problem by chain decomposition and tabu search, Transportation Science 29 (1995) 30.
 P.M. Hooper, Nearly orthogonal randomized designs, Journal of the Royal Statistical Society Series B — Methodological 55 (1993) 221.
 J.J. Hopfield and D. Tank, Computing with neural circuits. A model, Science 233 (1986) 624.
 J.J. Hopfield and D. Tank, Neural computations of decisions in optimization problems, Biological Cybernetics 52 (1985) 141.
 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. CrossRef
 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.
 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. CrossRef
 T.C. Hu, A.B. Khang and C.W.A. Tsao, Old bachelor acceptance. A new class of nonmonotonic threshold accepting methods, ORSA Journal on Computing 7 (1995) 417.
 N.F. Hu, Tabu search method with random moves for globally optimal design, International Journal for Numerical Methods in Engineering 35 (1992) 1055.
 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. CrossRef
 T. Huang, C. Zhang, S. Lee and H.P. Wang, Implementation and comparison of 3 neural network learning algorithms, Kybernetes 22 (1993) 22.
 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).
 M.L. Huber, Structural optimization of vaporpressure correlations using simulated annealing and threshold accepting. Application to R134A, Computers and Chemical Engineering 18 (1994) 929. CrossRef
 R. Hubscher and F. Glover, Applying tabu search with influential diversification to multiprocessor scheduling, Computers and Operations Research 21 (1994) 877. CrossRef
 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).
 C.L. Huntley and D.E. Brown, A parallel heuristic for quadratic assignment problems, Computers and Operations Research 18 (1991) 275. CrossRef
 J. Hurink, B. Jurisch and M. Thole, Tabu search for the jobshop scheduling problem with multipurpose machines, OR Spektrum 15 (1994) 205. CrossRef
 P. Husbands, An ecosystems model for integrated production planning, The International Journal of Computer Integrated Manufacturing 6 (1993) 74.
 P. Husbands, F. Mill and S. Warrington, Genetic algorithms, production plan optimization and scheduling,Lecture Notes in Computer Science 496 (1991) p. 80.
 Y. Ichikawa and T. Sawa, Neural network application for direct feedback controllers, IEEE Transactions on Neural Networks 3 (1992) 224. CrossRef
 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 2layer randomfield model. Meanfield approximation, Systems and Computers in Japan 25 (1994) 61.
 ILOG: ILOG Solver, Schedule, Collected papers, ILOG Headquarters, Gentilly, France (1994).
 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. CrossRef
 L. Ingber, Very fast simulated reannealing, Mathematical and Computer Modelling 12 (1989) 967. CrossRef
 L. Ingber, H. Fujio and M.F. Wehner, Mathematical comparison of combat computer models to exercise data, Mathematical and Computer Modelling 15 (1991) 65. CrossRef
 L. Ingber and B. Rosen, Genetic algorithms and very fast simulated reannealing. A comparison, Mathematical and Computer Modelling 16 (1992) 87. CrossRef
 L. Ingber, M.F. Wehner, G.M. Jabbour and T.M. Barnhill, Application of statistical mechanics methodology to term structure bondpricing models, Mathematical and Computer Modelling 15 (1991) 77. CrossRef
 H. Ishibuchi, S. Misaki and H. Tanaka, Modified simulated annealing algorithms for the flowshop sequencing problem, European Journal of Operational Research 81 (1995) 388. CrossRef
 H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka, Selection of fuzzy ifthen rules by a genetic method, Electronics and Communications in Japan Part III — Fundamental Electronic Science 77 (1994) 94.
 H. Ishibuchi, N. Yamamoto, S. Misaki and H. Tanaka, Local search algorithms for flowshop scheduling with fuzzy duedates, International Journal of Production Economics 33 (1994) 53. CrossRef
 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. CrossRef
 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.
 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. CrossRef
 L.W. Jacobs and M.J. Brusco, A localsearch heuristic for large setcovering problems, Naval Research Logistics 42 (1995) 1129.
 S.H. Jacobson, How difficult is the frequency selection problem, Operations Research Letters 17 (1995) 139. CrossRef
 A. Jagota, Approximating maximum clique with a Hopfield network, IEEE Transactions on Neural Networks 6 (1995) 724. CrossRef
 L.C. Jain, Hybrid connectionist systems in research and teaching, IEEE Aerospace and Electronic Systems Magazine 10 (1995) 14. CrossRef
 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.
 H. James, Software for studying and developing applications of artificial neural networks, Economic Journal 104 (1994) 181.
 M. Jampel, Constraint logic programming. A bibliography, Working paper, City University, London (1995).
 C.Z. Janikow, A knowledgeintensive genetic algorithm for supervised learning, Machine Learning 13 (1993) 189. CrossRef
 J. Jansen, R.C.M.H. Douven and E.E.M Vanberkum, An annealing algorithm for searching optimal blockdesigns, Biometrical Journal 34 (1992) 529.
 G.K. Janssens and A. van Breedam, A simulated annealing postprocessor for the vehicle routing problem, in:Applications of Modern Heuristic Methods, ed. V. RaywardSmith (Alfred Waller, HenleyonThames, 1995).
 B. Jaumard, P.S. Ow and B. Simeone, A selected artificial intelligence bibliography for operations researchers, Annals of Operations Research 12 (1988) 1.
 C. Jedrzejek and L. Cieplinski, Heuristic versus statistical physics approach to optimization problems, Acta Physica Polonica B 26 (1995) 977.
 D.E. Jeffcoat and R.L. Bulfin, Simulated annealing for resourceconstrained scheduling, European Journal of Operational Research 70 (1993) 43. CrossRef
 W. Jeffrey and R. Rosner, Optimization algorithms. Simulated annealing and neural network processing, Astrophysical Journal 310 (1986) 473. CrossRef
 C.S. Jeong and M.H. Kim, Fast parallel simulated annealing for traveling salesman problem on SIMDmachines with linear interconnections, Parallel Computing 17 (1991) 221. CrossRef
 L.M. Jin and S.P. Chan, A genetic approach for network partitioning, International Journal of Computer Mathematics 42 (1992a) 47.
 L.M. Jin and S.P. Chan, Analog placement by formulation of macrocomponents and genetic partitioning, International Journal of Electronics 73 (1992b) 157.
 S. Jockusch and H. Ritter, Selforganizing maps. Local competition and evolutionary optimization, Neural Networks 7 (1994) 1229. CrossRef
 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. CrossRef
 D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon, Optimization by simulated annealing. An experimental evaluation 2. Graphcoloring and number partitioning, Operations Research 39 (1991) 378.
 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.
 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.
 D.S. Johnson, C. Papadimitriou and M. Yannakis, How easy is local search, Journal of Computer and System Sciences 37 (1988) 79. CrossRef
 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).
 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. CrossRef
 A. Jones, L. Rabelo and Y.W. Yih, A hybrid approach for realtime sequencing and scheduling, International Journal of Computer Integrated Manufacturing 8 (1995) 145.
 A.E.W. Jones and G.W. Forbes, An adaptive simulated annealing algorithm for global optimization over continuousvariables, Journal of Global Optimization 6 (1995) 1. CrossRef
 A.J. Jones, Genetic algorithms and their applications to the design of neural networks, Neural Computing and Applications 1 (1993) 32. CrossRef
 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.
 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.
 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. CrossRef
 K. Jörnsten and A. Løkketangen, Tabu search for weightedKcardinality trees, Working paper M9303, Institute of Informatics, Molde College, Molde, Norway (1993).
 J. Jszefowska, G. Waligsra and J. Weglarz, A tabu search algorithm for some discretecontinuous scheduling problems, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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. CrossRef
 K. Juliff, A multichromosome 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.
 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.
 N. Kadaba, XROUTE. A knowledgebased 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.
 J.H. Kalivas, Optimization using variations of simulated annealing, Chemometrics and Intelligent Laboratory Systems 15 (1992) 1. CrossRef
 T. Kampke, Simulated annealing: Use of a new tool in bin packing, Annals of Operations Research 16 (1988) 327.
 S. Kaparthi and N.C. Suresh, Performance of selected partmachine grouping techniques for data sets of wide ranging sizes and imperfection, Decision Sciences 25 (1994) 515.
 S. Kaparthi, N.C. Suresh and R.P. Cerveny, An improved neuralnetwork leader algorithm for partmachine grouping in group technology, European Journal of Operational Research 69 (1993) 342. CrossRef
 A. Kapsalis, P. Chardaire, V.J. RaywardSmith 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. RaywardSmith and G.D. Smith, Solving the graphical Steiner tree problem using genetics, Journal of the Operational Research Society 44 (1993) 397.
 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.
 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. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 A.J. Keane, Genetic algorithm optimization of multipeak problems. Studies in convergence and robustness, Artificial Intelligence in Engineering 9 (1995) 75. CrossRef
 J.P. Kelly, B.L. Golden and A.A. Assad, Largescale controlled rounding using tabu search with strategic oscillation, Annals of Operations Research 41 (1993) 69. CrossRef
 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. CrossRef
 W. Kern, On the depth of combinatorial optimization problems, Discrete Applied Mathematics 43 (1993) 115. CrossRef
 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.
 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.
 M.D. Kidwell, Using genetic algorithms to schedule distributed tasks on a busbased system, in:Proceedings of the 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, 1993) p. 368.
 C. Kim, W. Kim, H. Shin, K. Rhee, H. Chung and J. Kim, Combined hierarchical placement algorithm for rowbased layouts, Electronics Letters 29 (1993) 1508.
 H. Kim, K. Nara and M. Gen, A method for maintenance scheduling using GA combined with SA, Computers and Industrial Engineering 27 (1994) 477. CrossRef
 Y.T. Kim, Y.J. Jang and M.W. Kim, Stepwise overlapped parallel annealing and its application to floorplan designs, ComputerAided Design 23 (1991) 133. CrossRef
 R.K. Kincaid, Solving the damper placement problem via local search heuristics, OR Spektrum 17 (1995) 149. CrossRef
 R.K. Kincaid, Minimizing distortion in truss structures. A comparison of simulated annealing and tabu search, Structural Optimization 5 (1993) 217. CrossRef
 R.K. Kincaid, Good solutions to discrete noxious location problems via metaheuristics, Annals of Operations Research 40 (1992) 265. CrossRef
 R.K. Kincaid, A.D. Martin and J.A. Hinkley, Heuristic search for the polymer straightening problem, Computational Polymer Science 5 (1995) 1.
 W. Kinnebrock, Accelerating the standard backpropagation method using a genetic approach, Neurocomputing 6 (1994) 583. CrossRef
 S. Kirkpatrick, C.D. Gelatt and P.M. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671.
 L.M. Kirousis, Fast parallel constraint satisfaction, Artificial Intelligence 64 (1993) 147. CrossRef
 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.
 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. CrossRef
 H. Kitano, Neurogenetic learning. An integrated method of designing and training neural networks using genetic algorithms, Physica D75 (1994) 225.
 R.W. Klein and K.C. Dubes, Experiments in projection and clustering by simulated annealing, Pattern Recognition 22 (1989) 213. CrossRef
 J.G. Klincewicz, Avoiding local optima in thephub location problem using tabu search and GRASP, Annals of Operations Research 40 (1992) 283. CrossRef
 J.G. Klincewicz and A. Rajan, Using GRASP to solve the component grouping, Working paper, AT&T Bell Laboratories, Holmdel, New Jersey (1992).
 J. Knox, Tabu search performance on the symmetrical traveling salesman problem, Computers and Operations Research 21 (1994) 867. CrossRef
 J. Knox, The application of tabu search to the symmetric traveling, Ph.D. Dissertation, Graduate School of Business, University of Colorado, Boulder (1989).
 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.
 S.J. Koh and C.Y. Lee, A tabu search for the survivable fiber optic communicationnetwork design, Computers and Industrial Engineering 28 (1995) 689. CrossRef
 T. Kohonen,SelfOrganizing and Associative Memory, 3rd edition (Springer, Berlin, 1992).
 T. Kohonen, Selforganized formation of topological correct feature maps, Biological Cybernetics 43 (1982) 59. CrossRef
 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. CrossRef
 A. Kolen and E. Pesch, Genetic local search in combinatorial optimization, Discrete Applied Mathematics 48 (1994) 273. CrossRef
 M. Kolonko, A piecewise Markovian model for simulated annealing with stochastic cooling schedules, Journal of Applied Probability 32 (1995) 649.
 G. Kontoravdis and J.F. Bard, Improved heuristics for the vehicle routing problem with time windows, ORSA Journal on Computing 7 (1995) 10.
 H. Kopfer, Concepts of genetic algorithms and their application to the freight optimization problem in commercial shipping, OR Spektrum 14 (1992) 137. CrossRef
 H. Kopfer, G. Pankratz and E. Erkens, Development of a hybrid genetic algorithm for vehiclerouting, OR Spektrum 16 (1994) 21. CrossRef
 E. Korutcheva, M. Opper and B. Lopez, Statistical mechanics of the knapsack problem, Journal of Physics A — Mathematical and General 27 (1994) 645. CrossRef
 C. Koulmas, S.R. Antony and R. Jaen, A survey of simulated annealing applications to operations research problems, Omega 22 (1994) 41. CrossRef
 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.
 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.
 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. CrossRef
 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.
 M. Kovacic, Timetable construction with markovian neural network, European Journal of Operational Research 69 (1993) 92. CrossRef
 J.R. Koza,Genetic Programming. On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA 1992).
 J.R. Koza,Genetic Programming II. Automatic Discovery of Reusable Subprograms (MIT Press, Cambridge, MA 1994).
 S.A. Kravitz and R.A. Rutenbar, Placement by simulated annealing on a multiprocessor, IEEE Transaction on Computer Aided Design 6 (1987) 534. CrossRef
 V. Kreinovich, C. Quintana and O. Fuentes, Genetic algorithms. What fitness scaling is optimal, Cybernetics and Systems 24 (1993) 9.
 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.
 B. Kroger, Guillotineable bin packing. A genetic approach, European Journal of Operational Research 84 (1995) 645. CrossRef
 B. Kroger, P. Schwenderling and O. Vornberger, Parallel genetic packing of rectangles,Lecture Notes in Computer Science 496 (1991) p. 160.
 L. Kryzanowski, M. Galler and D.W. Wright, Using artificial neural networks to pick stocks, Financial Analysts Journal 49 (1993) 21.
 H.M. Ku and I.M. Karimi, An evaluation of simulated annealing for batch process scheduling, Industrial and Engineering Chemistry Research 30 (1991) 163. CrossRef
 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.
 R. Kuik and M. Salomon, Multilevel lotsizing problem. Evaluation of a simulated annealing heuristic, European Journal of Operational Research 45 (1990) 25. CrossRef
 U.R. Kulkarni and M.Y. Kiang, Dynamic grouping of parts in flexible manufacturing systems. A selforganizing neural networks approach, European Journal of Operational Research 84 (1995) 192. CrossRef
 V. Kumar, Algorithms for constraint satisfaction problems. A survey, AI Magazine 13 (1992) 32.
 M. Kuroda and A. Kawada, Improvement on the computational efficiency of inverse queueing network analysis, Computers and Industrial Engineering 27 (1994) 421. CrossRef
 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.
 V. Kvasnicka and J. Pospichal, Messay simulated annealing, Journal of Chemometrics 9 (1995) 309. CrossRef
 V. Kvasnicka and J. Pospichal, Fast evaluation of chemical distance by tabu search algorithm, Journal of Chemical Information and Computer Sciences 34 (1994) 1109. CrossRef
 M. Laguna, Clustering for the design of sonet rings in interoffice telecommunications, Management Science 40 (1994) 1533.
 M. Laguna, Tabu search primer, Working paper, Graduate School of Business, University of Colorado, Boulder (1993).
 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. CrossRef
 M. Laguna, J.W. Barnes and F. Glover, Tabu search methods for a singlemachine scheduling problem, Journal of Intelligent Manufacturing 2 (1991) 63. CrossRef
 M. Laguna and F. Glover, Bandwidth packing. A tabu search approach, Management Science 39 (1993) 492.
 M. Laguna and F. Glover, Integrating target analysis and tabu search for improved scheduling systems, Expert Systems with Applications 6 (1993a) 287. CrossRef
 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. CrossRef
 M. Laguna and P. Laguna, Applying tabu search to the 2dimensional Ising spinglass, International Journal of Modern Physics C — Physics and Computers 6 (1995) 11. CrossRef
 M. Laguna and J.L.G. Velarde, A search heuristic for justintime scheduling in parallel machines, Journal of Intelligent Manufacturing 2 (1991) 253. CrossRef
 G. Laporte and I.H. Osman,Metaheuristics in Combinatorial Optimization, Annals of Operations Research 63 (Baltzer, Basel, 1996).
 G. Laporte and I.H. Osman, Routing problems. A biliography, Annals of Operations Research 61 (1995) 227. CrossRef
 S. Lash, Genetic algorithms for weighted tardiness scheduling on parallel machines, Working paper 9301, Department of Industrial Engineering, and Management Sciences, Northwestern University, Evanston, Illinois (1993).
 J.B. Lasserre, P.P. Varaiya and J. Walrand, Simulated annealing, random search, multistart or sad, Systems and Control Letters 8 (1987) 297. CrossRef
 C. Lau,Neural Networks. Theoretical Foundations and Analysis (IEEE Computer Society Press, Los Alamitos, California, 1992).
 P.S. Laursen, An experimental comparison of 3 heuristics for the WVCP, European Journal of Operational Research 73 (1994) 181. CrossRef
 P.S. Laursen, Simulated annealing for the QAP. Optimal tradeoff between simulation time and solution quality, European Journal of Operational Research 69 (1993) 238. CrossRef
 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).
 R. Leardi, R. Boggia and M. Terrile, Genetic algorithms as a strategy for featureselection, Journal of Chemometrics 6 (1992) 267. CrossRef
 J.P. Leclercq and V. Englebert, Automatic graph's drawing, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 33.
 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. CrossRef
 B.W. Lee and B.J. Sheu,Hardware Annealing in Analog VLSI Neurocomputing (Kluwer, Boston, 1991).
 C.K. Lee and K.I. Yang, Network design of oneway streets with simulated annealing, Papers in Regional Science 73 (1994) 119.
 C.Y. Lee, Genetic algorithms for singlemachine job scheduling with common duedate and symmetrical penalties, Journal of the Operations Research Society of Japan 37 (1994) 83.
 C.Y. Lee and J.Y. Choi, A genetic algorithm for job sequencing problems with distinct duedates and general earlytardy penalty weights, Computers and Operations Research 22 (1995) 857. CrossRef
 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. CrossRef
 F.H. Lee, G.S. Stiles and V. Swaminathan, Parallel annealing on distributed memory systems, Programming and Computer Software 21 (1995) 1.
 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.
 J. Lee, J.H. Chou and S.L. Fu, New approach for the ordering of gate permutation in onedimensional logicarrays, IEE Proceedings — Circuits Devices and Systems 142 (1995) 90. CrossRef
 J.Y. Lee and M.Y. Choi, Optimization by multicanonical annealing and the traveling salesman problem, Physical Review E50 (1994) 651.
 S. Lee and H.P. Wang, Modified simulated annealing for multipleobjective engineering design optimization, Journal of Intelligent Manufacturing 3 (1992) 101. CrossRef
 Y.N. Lee, G.P. McKeown and V.J. RaywardSmith, The convoy movement problem with initial delays, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 V.J. Leon and R. Balakrishnan, Strength and adaptibility of problemspace based neighborhoods for resource constrained scheduling, OR Spektrum 17 (1995) 173. CrossRef
 C. LePape, Constraintbased programming for scheduling. An historical perspective, Working paper, ILOG, Gentilly, France (1994).
 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.
 Y.Y. Leu, L.A. Matheson and L.P. Rees, Assemblyline balancing using genetic algorithms with heuristicgenerated initial populations and multiple evaluation criteria, Decision Sciences 25 (1994) 581.
 J. Lever, M. Wallace and B. Richards, Constraint logic programming for scheduling and planning, BT Technology Journal 13 (1995) 73.
 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).
 D.M. Levine, PGAPack V0.2: A DataStructure 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).
 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).
 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.
 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.
 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. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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).
 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).
 G.E. Liepins G.E. Hilliard and M. Hilliard, Genetic algorithms. Foundations and applications, Annals of Operations Research 21 (1989) 31. CrossRef
 G.E. Liepins and M.D. Vose, Characterizing crossover in genetic algorithms, Annals of Mathematics and Artificial Intelligence 5 (1991) 27. CrossRef
 G.E. Liepins and M.D. Vose, Representational issues in genetic optimization, Journal of Experimental and Theoretical Artificial Intelligence 2 (1990) 101.
 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. CrossRef
 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.
 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. CrossRef
 C.T. Lin and C.S.G. Lee, A multivalued Boltzmann machine, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 660.
 F.T. Lin, C.Y. Kao and C.C. Hsu, Applying the genetic approach to simulated annealing in solving some NPhard problems, IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 1752.
 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.
 S.C. Lin and J.H.C. Hsueh, Nearest neighbor heuristics in accelerated algorithms of optimization problems, Physica A203 (1994b) 369.
 R.P. Lippmann, An introduction to computing with neural nets, IEEE ASSP Magazine 4 (1987) 4.
 Y. Lirov, Computeraided neural network engineering, Neural Networks 5 (1992a) 711.
 Y. Lirov, Knowledge based approach to the cutting stock problem, Mathematical and Computer Modelling 16 (1992b) 107. CrossRef
 P.G.J. Lisboa,Neural Networks: Current Applications (Chapman and Hall, London, 1992).
 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.
 J. Little and K. DarbyDowman, 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).
 C.M. Liu, R.L. Kao and A.H. Wang, Solving locationallocation problems with rectilinear distances by stimulated annealing, Journal of the Operational Research Society 45 (1994) 1304.
 C.M. Liu and J.K. Wu, Machine cellformation using the simulated annealing algorithm, International Journal of Computer Integrated Manufacturing 6 (1993) 335.
 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. CrossRef
 Z.P. Lo and B. Bavarian, Optimization of job scheduling on parallel machines by simulated annealing algorithms, Expert Systems with Applications 4 (1992) 323. CrossRef
 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.
 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.
 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.
 A. Løkketangen, Tabu search. Using the search experience to guide the search process. An introduction with examples, AI Communications 8 (1995) 78.
 A. Løkketangen and F. Glover, Probabilistic move selection in tabu search for zeroone mixed integer programming problems, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 A. Løkketangen and F. Glover, Tabu search for zeroone 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. CrossRef
 C.K. Looi, Neural network methods in combinatorial optimization, Computers and Operations Research 19 (1992) 191. CrossRef
 H.R. Lourenço, Job shop scheduling. Computational study of local search and largestep optimization methods, European Journal of Operational Research 83 (1995) 347. CrossRef
 H.R. Lourenço and M. Zwijnenburg, Combining the largestep optimization with tabusearch. Application to the job shop scheduling problem, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 C.B. Lucasius and G. Kateman, Understanding and using genetic algorithms 1. Concepts, properties and context, Chemometrics and Intelligent Laboratory Systems 19 (1993) 1. CrossRef
 C.B. Lucasius and G. Kateman, Understanding and using genetic algorithms 2. Representation, configuration and hybridization, Chemometrics and Intelligent Laboratory Systems 25 (1994a) 99. CrossRef
 C.B. Lucasius and G. Kateman, Gates towards evolutionary largescale optimization. A softwareoriented approach to genetic algorithms 1. General perspective, Computers and Chemistry 18 (1994b) 127. CrossRef
 C.B. Lucasius and G. Kateman, Gates towards evolutionary largescale optimization. A softwareoriented approach to genetic algorithms 2. Toolbox description, Computers and Chemistry 18 (1994c) 137. CrossRef
 S. Lundy and A. Mees, Convergence of an annealing algorithm, Mathematical Programming 34 (1986) 111. CrossRef
 H. Lutfiyya, B. McMillin, P. Poshyanonda and C. Dagli, Composite stock cutting through simulated annealing, Mathematical and Computer Modelling 16 (1992) 57. CrossRef
 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).
 J.L. Lutton and E. Bonomi, Simulated annealing algorithm for the minimum weighted perfect Euclidean matching problem, RAIRO — Operations Research 20 (1986) 177.
 A.K. Mackworth and E.C. Freuder, The complexity of constraint satisfaction revisited, Artifical Intelligence 59 (1993) 57. CrossRef
 A.K. Mackworth, Constraint satisfaction, in:Encyclopedia of Artificial Intelligence, Vol. 1, ed. S.C. Shaprio (Wiley, Chichester, 1992a).
 A.K. Mackworth, The logic of constraint satisfaction, Artificial Intelligence 58 (1992b) 3. CrossRef
 S.W. Mahfoud, Finite Markov chain models of an alternative selection strategy for the genetic algorithm, Complex Systems 7 (1993) 493.
 S.W. Mahfoud and D.E. Goldberg, Parallel recombinative simulated annealing. A genetic algorithm, Parallel Computing 21 (1995) 1. CrossRef
 D. Maio, D. Maltoni and S. Rizzi, Topological clustering of maps using a genetic algorithm, Pattern Recognition Letters 16 (1995) 89. CrossRef
 A.K. Majhi, L.M. Patnaik and S. Raman, A genetic algorithmbased circuit partitioner for MCMS, Microprocessing and Microprogramming 41 (1995) 83. CrossRef
 E. Makinen and M. Sieranta, Genetic algorithms for drawing bipartite graphs, International Journal of Computer Mathematics 53 (1994) 157.
 B. Malakooti, J. Wang and E.C. Tandler, A sensorbased accelerated approach for multiattribute machinability and tool life evaluation, International Journal of Production Research 28 (1990) 2373.
 B. Malakooti and Y.Q. Zhou, Feedforward artificial neural networks for solving discrete multiple criteria decision making problems, Management Science 40 (1994) 1542.
 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. CrossRef
 C.J. Malmborg, Optimization of cubeperorder index warehouse layouts with zoning constraints, International Journal of Production Research 33 (1995) 465.
 C.J. Malmborg, Heuristic, storage space minimisation methods for facility layouts served by looped AGV systems, International Journal of Production Research 32 (1994) 2695.
 R.J. Mammone and Y.Y. Zeevi,Neural Networks: Theory and Applications (Academic Press, London, 1991).
 J. Mandziuk, Solving thenqueens problem with a binary Hopfield type network. Synchronous and asynchronous model, Biological Cybernetics 72 (1995) 439. CrossRef
 V. Maniezzo, Genetic evolution of the topology and weight distribution of neural networks, IEEE Transactions on Neural Networks 5 (1994) 39. CrossRef
 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. CrossRef
 J.W. Mann, A. Kapsalis and G.D. Smith, The GAmeter toolkit, in:Applications of Modern Heuristic Methods, ed. V.J. RaywardSmith (Alfred Waller, HenleyonThames, 1995).
 J.W. Mann and G.D. Smith, A comparison of heuristics for telecommunications traffic routing, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 N. Mansour, Parallel physical optimization algorithms for allocating data to multicomputer nodes, Journal of Supercomputing 8 (1994) 53. CrossRef
 N. Mansour and G.C. Fox, Allocating data to distributed memory multiprocessors by genetic algorithms, Concurrency — Practice and Experience 6 (1994) 485.
 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.
 N. Mansour and G.C. Fox, Parallel physical optimization algorithms for data mapping,Lecture Notes in Computer Science 634 (1992b) p. 91.
 R. Marett and M. Wright, A comparison of neighbourhood search techniques for multiobjective combinatorial problems, Working paper, Department of Management Science, University of Lancaster, England (1994).
 E. Marinari and G. Parisi, Simulated tempering. A new Monte Carlo scheme, Europhysics Letters 19 (1992) 451.
 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).
 O.C. Martin and S.W. Otto, Combining simulated annealing with local search heuristics, Annals of Operations Research 63 (1996) 57.
 O.C. Martin and S.W. Otto, Partitioning of unstructured meshes for load balancing, Concurrency — Practice and Experience 7 (1995) 303.
 O.C. Martin, S.W. Otto and E.W. Felten, Largestep Markov chains for the TSP, incorporating local search heuristics, Operations Research Letters 11 (1992) 219. CrossRef
 O.C. Martin, S.W. Otto and E.W. Felten, Largestep Markov chains for the TSP, Complex Systems 5 (1991) 299.
 F. Maruyama, Y. Minoda and S. Sawada, A logical framework for constraint programming, Fujitsu Scientific and Technical Journal 30 (1994) 69.
 R. Mason, R. Gunst and J. Hess,Statistical Design and Analysis of Experiments (Wiley, Chichester, 1989).
 R. Mathar and A. Zilinskas, On global optimization in 2dimensional scaling, Acta Applicandae Mathematicae 33 (1993) 109. CrossRef
 R. Mathar and J. Mattfeldt, Channel assignment in cellular radio networks, IEEE Transactions on Vehicular Technology 42 (1993) 647. CrossRef
 R. Mathieu, L. Pittard and G. Anandalingam, Genetic algorithm based approach to bilevel linear programming, RAIRO — Operations Research 28 (1994) 1.
 Y. Matsuyama, Selforganization neural networks and various Euclidean travelling salesman problems, Systems and Computers in Japan 23 (1992) 101.
 Y. Matsuyama, Selforganization via competition, cooperation and categorization applied to extended vehicle routing problems, in:Proceedings of the International Joint Conference on Neural Networks, Seattle, WA (1991) p. I385.
 M. Matysiak, Efficient optimization of large join queries using tabu search, Information Sciences 83 (1995) 77. CrossRef
 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).
 E. Mayrand, P. Lefrançois, O. Kettani and M.H. Jobin, A genetic search algorithm to optimize job sequencing under a technological constraint in a rollingmill facility, OR Spektrum 17 (1995) 183. CrossRef
 C. Mazza, Parallel simulated annealing, Random Structures and Algorithms 3 (1992) 139.
 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.
 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. CrossRef
 C.C. McGeoch, Experimental analysis of algorithms, Ph.D. Dissertation, CMUCS87124, Computer Science Department, Carnegie Mellon University (1986).
 I.I. Melamed, Neural networks and combinatorial optimization, Automation and Remote Control 55 (1994) 1553.
 J.B.M. Melissen and P.C. Schuur, Packing 16, 17 or 18 circles in an equilateral triangle, Discrete Mathematics 145 (1995) 333. CrossRef
 F. Menczer and D. Parisi, Evidence of hyperplanes in the genetic learning of neural networks, Biological Cybernetics 66 (1992a) 283. CrossRef
 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. CrossRef
 F. Menezes and P. Barahona, Heuristics and lookahead integration to solve constraint satisfaction problems efficiently, Annals of Operations Research 50 (1994) 411. CrossRef
 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. CrossRef
 P. Meseguer, Constraint satisfaction problems. An overview, AI Communications 2 (1989) 3.
 Z. Michalewicz, Evolutionary computation and heuristics, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston 1996).
 Z. Michalewicz, Evolutionary computation techniques for nonlinear programming problems, International Transactions in Operational Research 1 (1994a) 223. CrossRef
 Z. Michalewicz, Nonstandard methods in evolutionary computation, Statistics and Computing 4 (1994b) 141.
 Z. Michalewicz, A hierarchy of evolution programs. An experimental study, Evolutionary Computation 1 (1993) 51.
 Z. Michalewicz,Genetic Algorithms + Data Structures = Evolution Programs (Springer, Berlin, 1992).
 Z. Michalewicz, G.A. Vignaux and M. Hobbs, A nonstandard genetic algorithm for nonlinear transportation problem, ORSA Journal on Computing 3 (1991) 307.
 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. CrossRef
 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.
 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. CrossRef
 L. Miclo, Remarks on ergodicity of simulated annealing algorithms on a graph, Stochastic Processes and their Applications 58 (1995) 329. CrossRef
 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.
 G.F. Miller, P.M. Todd and S.U. Hedge, Designing neural networks, Neural Networks 4 (1991) 53. CrossRef
 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. CrossRef
 M. Minagawa and Y. Kakazu, A genetic approach to the line balancing problem, IFIP Transactions B — Applications in Technology 3 (1992) 737.
 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. CrossRef
 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. CrossRef
 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.
 M. Mitchell,An Introduction to Genetic Algorithms (The MIT Press, Cambridge, 1996).
 M. Mitchell, and J.H. Holland, When will a genetic algorithm outperform hillclimbing?, Working paper, Santa Fe Institute, Santa Fe, New Mexico (1993).
 D. Mitra, F. Romeo and A. SangiovanniVincentelli, Convergence and finitetime behavior of simulated annealing, Advances in Applied Probability 18 (1986) 747.
 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.
 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. CrossRef
 J.V. Moccellin, A new heuristic method for the permutation flowshop scheduling problem, Journal of the Operational Research Society 46 (1995) 883.
 P. Molitor, Layer assignment by simulated annealing, Microprocessing and Microprogramming 16 (1985) 345. CrossRef
 Y.B. Moon and S.C. Chi, Generalized part family formation using neural network techniques, Journal of Manufacturing Systems 11 (1992) 149.
 E.L. Mooney and R.L. Rardin, Tabu search for a class of scheduling problems, Annals of Operations Research 41 (1993) 253. CrossRef
 L.B. Morales, R. Gardunojuarez and D. Romero, The multipleminima problem in small peptides revisited. The threshold accepting approach, Journal of Biomolecular Structure and Dynamics 9 (1992) 951.
 K. Morizawa, H. Nagasawa and N. Nishiyama, Complex random sample scheduling and its application to anN/M/F/Fmax problem, Computers and Industrial Engineering 27 (1994) 23. CrossRef
 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).
 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. CrossRef
 P. Moscato and J.F. Fontanari, Stochastic versus deterministic update in simulated annealing, Physics Letters A146 (1990) 204.
 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).
 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 (NorthHolland, Amsterdam, 1992).
 H. Mühlenbein, Parallel genetic algorithms, population genetics and combinatorial optimization,Lecture Notes in Artificial Intelligence 565 (1991) p. 398.
 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.
 H. Mühlenbein, M. GorgesSchleuter and O. Krämer, New solutions to the mapping problem of parallel systems. The evolution approach, Parallel Computing 4 (1987) 269. CrossRef
 H. Mühlenbein and D. SchlierkampVoosen, Analysis of selection, mutation and recombination in genetic algorithms,Lecture Notes in Computer Science 899 (1995) p. 142.
 H. Mühlenbein and D. SchlierkampVoosen, The science of breeding and its application to the breeder genetic algorithm, Evolutionary Computation 1 (1994) 335.
 H. Mühlenbein and D. SchlierkampVoosen, Predictive models for the breeder genetic algorithm I. Continuous parameter optimization, Evolutionary Computation 1 (1993) 25.
 H. Mühlenbein, M. Schomisch and J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (1991) 619. CrossRef
 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).
 H. Mulkens, Revisiting the Johnson algorithm for flowshop scheduling with genetic algorithms, IFIP Transactions B — Applications in Technology 15 (1994) 69.
 B. Muller and J. Reinhardt,Neural Networks: An Introduction (Springer, Berlin, 1991).
 A.T. Murray and R.L. Church, Heuristic solution approaches to operational forest planning problems, OR Spektrum 17 (1995) 193. CrossRef
 C.V.R. Murthy and G. Srinivasan, Fractional cellformation in group technology, International Journal of Production Research 33 (1995) 1323.
 K.L. Musser, J.S. Dhingra and G.L. Blankenship, Optimization based job shop scheduling, IEEE Transactions on Automatic Control 38 (1993) 808. CrossRef
 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.
 A. Nagar, S.S. Heragu and J. Haddock, A combined branchandbound and genetic algorithm based approach for a flowshop scheduling problem, Annals of Operations Research 63 (1996) 397.
 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.
 K. Naphade, S.D. Wu and R.H. Storer, A problem space method for the resource constraint project scheduling problem, Working paper 94T005, Department of Industrial Engineering, Lehigh University, Bethlehem, PA (1994).
 M.M. Nelson and W.T. Illingworth,A Practical Guide to Neural Nets (AddisonWesley, Wokingham, England, 1991).
 G.L. Nemhauser and L.A. Wolsey,Integer and Combinatorial Optimization (Wiley, Chichester, 1988).
 J.A. Nestor and G. Krishnamoorthy, SALSA. A new approach to scheduling with timing constraints, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 12 (1993) 1107. CrossRef
 N.K. Nguyen, An algorithm for constructing optimal resolvable incomplete block designs, Communications in Statistics — Simulation and Computation 22 (1993) 911.
 Y. Nishibe, K. Kuwabara, M. Yokoo and T. Ishida, Speedup and application distributed constraint satisfaction to communication network path assignments, Systems and Computers in Japan 25 (1994) 54.
 V. Nissen, Solving the quadratic assignment problem with clues from nature, IEEE Transactions on Neural Networks 5 (1994) 66. CrossRef
 V. Nissen, Evolutionary algorithms in mangement science. An overiew and list of references, Report No. 9303, Universität Göttingen, Germany (1993).
 V. Nissen and H. Paul, A modification of threshold accepting and its application to the quadratic assignment problem, OR Spektrum 17 (1995) 205. CrossRef
 A. Nix and M. Vose, Modelling genetic algorithms with Markov chains, Annals of Mathematics and Artificial Intelligence 5 (1992) 88. CrossRef
 S. Nolfi, D. Parisi and J.L. Elman, Learning and evolution in neural networks, Adaptive Behavior 3 (1994) 5.
 B.A. Norman and J.C. Bean, Random keys genetic algorithm for job shop scheduling, Working paper 945, Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor (1995).
 S. Norre, Task scheduling on a multiprocessor system. Deterministic models and stochastic models, RAIRO — Operations Research 28 (1994) 221.
 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).
 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).
 W.P.M. Nuijten, Time and resource constrained scheduling: A constraint satisfaction approach, Ph.D. Dissertation, Eindhoven University of Technology, Eindhoven, The Netherlands (1994).
 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
 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).
 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.
 K.E. Nygard, P. Juell and N. Kadaba, Neural networks for selecting vehicle routing heuristics, ORSA Journal on Computing 2 (1990) 353.
 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).
 F.A. Ogbu and D.K. Smith, Simulated annealing for the permutation flowshop problem, Omega 19 (1991) 64. CrossRef
 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. CrossRef
 M. Ohlsson, C. Peterson and B. Soderberg, Neural networks for optimization problems with inequality constraints. The knapsack problem, Neural Computation 5 (1993) 331.
 J.C. Oliveira, J.S. Ferreira and R.V.V. Vidal, Solving reallife combinatorial optimization problems using simulated annealing, Belgian Journal of Operations Research, Statistics and Computer Science 33 (1993) 49.
 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.
 S. Openshaw and L. Rao, Algorithms for reengineering 1991 census geography, Environment and Planning A 27 (1995) 425.
 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.
 I.H. Osman, An introduction to metaheuristics, in:Operational Research Tutorial Papers, ed. M. Lawrence and C. Wilson (Operational Research Society Press, Birmingham, 1995a).
 I.H. Osman, Heuristics for the generalized assignment problem. Simulated annealing and tabu search approaches, OR Spektrum 17 (1995b) 211. CrossRef
 I.H. Osman, Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problems, Annals of Operations Research 41 (1993a) 421. CrossRef
 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.
 I.H. Osman, Metastrategy simulated annealing and tabu search algorithms for combinatorial optimization problems, Ph.D. Thesis, The Management School, Imperial College, London (1991).
 I.H. Osman and N. Christofides, Capacitated clustering problems by hybrid simulated annealing and tabu search, International Transactions in Operational Research 1 (1994) 317. CrossRef
 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).
 I.H. Osman and J.P. Kelly,Metaheuristics. Theory and Applications (Kluwer, Boston, 1996b).
 I.H. Osman and C.N. Potts, Simulated annealing for permutation flowshop scheduling, Omega 17 (1989) 551. CrossRef
 I.H. Osman and S. Salhi, Local search strategies for the mix fleet vehicle routing problem, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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. CrossRef
 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 (NorthHolland, Amsterdam, 1992).
 R.H.J.M. Otten and L.P.P.P. van Ginneken,The Annealing Algorithm (Kluwer, Boston, 1989).
 S. Ottner, Genetic algorithms at Channel4, Expert Systems 11 (1994) 47.
 R. Padman, Choosing solvers in decision support systems. A neural network application in resourceconstrained project scheduling, in:Recent Developments in Decision Support Systems (Springer, Berlin, 1993) p. 559.
 L. Painton and J. Campbell, Genetic algorithms in optimization of system reliability, IEEE Transactions on Reliability 44 (1995) 172. CrossRef
 L. Painton and U. Diwekar, Stochastic annealing for synthesis under uncertainty, European Journal of Operational Research 83 (1995) 489. CrossRef
 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).
 K.F. Pal and D. Bhandari, Selection of optimal set of weights in a layered network using genetic algorithms, Information Sciences 80 (1994) 213. CrossRef
 K.F. Pal, Genetic algorithms for the traveling salesman problem based on a heuristic crossover operation, Biological Cybernetics 69 (1993) 539. CrossRef
 J. Pannetier, Simulated annealing. An introductory review, Institute of Physics Conference Series 107 (1990) 23.
 C.H. Papadimitriou, The complexity of the LinKernighan heuristic for the traveling salesman problem, SIAM Journal on Computing 21 (1992) 450. CrossRef
 C.H. Papadimitriou and K. Steiglitz,Combinatorial Optimization. Algorithms and Complexity (PrenticeHall, Englewood Cliffs, 1982).
 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.
 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).
 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).
 P.M. Pardalos and J. Xue, The maximum clique problem, Journal of Global Optimization 4 (1994) 301. CrossRef
 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 (NorthHolland, Amsterdam, 1992).
 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.
 J. Paulli, A computational comparison of simulated annealing and tabu search applied to the quadratic assignment problem, TimeNew York 396 (1993) 85.
 J. Pearl,Heuristics: Intelligent Search Strategies for Computer Problem Solving (AddisonWesley, Wokingham, England, 1984).
 J.F. Pekny and D.I. Miller, Exact solution of the nowait flowshop scheduling problem with a comparison to heuristic methods, Computers and Chemical Engineering 15 (1991) 741. CrossRef
 M.P. Pensini, G. Mauri and F. Gardin, Flowshop and TSP,Lecture Notes in Artificial Intelligence 565 (1991) p. 157.
 S.J. Perantonis and D.A. Karras, An efficient constrained learning algorithm with momentum acceleration, Neural Networks 8 (1995) 237. CrossRef
 E. Pesch, Machine learning by schedule decomposition, Working Paper RM 93045, Faculty of Economics and Business Administration, University of Limburg, The Netherlands (1993).
 E. Pesch and S. Voß, Applied local search. A prologue. Strategies with memories. Local search in application oriented environment, OR Spektrum 17 (1995) 55. CrossRef
 C. Peterson, Solving optimization problems with meanfield methods, Physica A200 (1993) 570.
 C. Peterson, Parallel distributed approaches to combinatorial optimization. Benchmark studies on travelling salesman problem, Neural Computation 2 (1990) 261.
 C. Peterson and B. Soderberg, A new method for mapping optimization problems onto neural networks, International Journal of Neural Systems 1 (1989) 3. CrossRef
 C. Peterson and J.R. Anderson, Neural network and NPcomplete optimization problems. A performance study on the graph bisection problem, Complex Systems 2 (1988) 59.
 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.
 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.
 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. CrossRef
 D.T. Pham and H.H. Onder, A knowledgebased system for optimizing workplace layouts using a genetic algorithm, Ergonomics 35 (1992) 1479.
 P.R. Philipoom, L.P. Pees and L. Wiegmann, Using neural networks to determine internally set duedate assignments for shop scheduling, Decision Sciences 26 (1995) 2.
 M. Piccioni, A combined multistart annealing algorithm for continuous global optimization, Computers and Mathematics with Applications 21 (1991) 173. CrossRef
 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. CrossRef
 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).
 M. Pirlot, General local search heuristics in combinatorial optimization. A tutorial, Belgian Journal of Operations Research, Statistics and Computer Science 32 (1993) 7.
 S. Poljak, Integer linear programs and local search for maxcut, SIAM Journal on Computing 24 (1995) 822. CrossRef
 J. Popovic, Vehicle routing in the case of uncertain demand. A Bayesian approach, Transportation Planning and Technology 19 (1995) 19.
 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.
 J.Y. Potvin, Genetic algorithms for the travelling salesman problem, Annals of Operations Research 63 (1996) 339.
 J.Y. Potvin, Neural networks stateoftheart survey. The travelling salesman problem, ORSA Journal on Computing 5 (1993) 328.
 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).
 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. CrossRef
 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).
 J.Y. Potvin and J.M. Rousseau, Constraint directed search for the advanced requesy dialaride 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).
 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. CrossRef
 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).
 C.C. Price and P.M. Shah, Optimization by simulated annealing. Experimental application to quadratic assignment problems, Texas Journal of Science 42 (1990) 215.
 C. Prins, Two scheduling problems in satellite telecommunications, RAIRO — Operations Research 25 (1991) 341.
 P. Prosser, Hybrid algorithms for the constraint satisfaction problem, Computational Intelligence 9 (1993) 268.
 A. Prugelbennet and J.L. Shapiro, Analysis of genetic algorithms using statistical mechanics, Physical Review Letters 72 (1994) 1305. CrossRef
 J.F. Puget, On the satisfiability of symmetrical constrained satisfaction problems, Working paper, ILOG SA, Gentilly, France (1993)
 J.F. Puget, Object oriented constraint programming for transportation problems, Working paper, ILOG SA, Gentilly, France (1992).
 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.
 A.P. Punnen and Y.P. Aneja, Categorized assignment scheduling. A tabu search approach, Journal of the Operational Research Society 44 (1993) 673.
 B. Purohit, T. Clark and T. Richards, Techniques for routing and scheduling services on a transmission network, BT Technology Journal 13 (1995) 64.
 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. CrossRef
 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. CrossRef
 F. Qian and H. Hirata, A parallel computation based on meanfield theory for combinatorial optimization and Boltzmann machines, Systems and Computers in Japan 24 (1994) 86.
 T.S. Raghu and C. Rajendran, Duedate setting methodologies based on simulated annealing. An experimental study in a reallife jobshop, International Journal of Production Research, 33 (1995) 2535.
 S. Rajasekaran and J.H. Reif, Nested annealing. A provable improvement to simulated annealing, Theoretical Computer Science 99 (1992) 157. CrossRef
 J. Ramanujam and P. Sadayappan, Mapping combinatorial optimization problems onto neural networks, Information Sciences 82 (1995) 239. CrossRef
 R.L. Rao and S.S. Iyengar, Bin packing by simulated annealing, Computers and Mathematics with Applications 27 (1994) 71. CrossRef
 R.L. Rardin and M. Sudit, Paroid search. Generic local combinatorial optimization, Discrete Applied Mathematics 43 (1993) 155. CrossRef
 C.P. Ravikumar, Parallel searchandlearn technique for solving largescale traveling salesperson problems, KnowledgeBased Systems 7 (1994) 169. CrossRef
 C.P. Ravikumar, Parallel techniques for solving largescale traveling salesperson problems, Microprocessors and Microsystems 16 (1992) 149. CrossRef
 C.P. Ravikumar and L.M. Patnaik, Performance improvement of simulated annealing algorithms, Computer Systems Science and Engineering 5 (1990) 111.
 C.P. Ravikumar and N. Vedi, Heuristic and neural algorithms for mapping tasks to a reconfigurable array, Microprocessing and Microprogramming 41 (1995) 137. CrossRef
 V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith,Modern Heuristic Search Methods (Wiley, Chichester, 1996).
 V.J. RaywardSmith,Applications of Modern Heuristic Methods (Alfred Waller, HenleyonThames, 1995a).
 V.J. RaywardSmith, A unified approach to tabu search, simulated annealing and genetic algorithms, in:Applications of Modern Heuristic Methods, ed. V.J. RaywardSmith (Alfred Waller, HenleyonThames, 1995b).
 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. CrossRef
 C.R. Reeves, Hybrid genetic algorithms for binpacking and related problems, Annals of Operations Research 63 (1996a) 371.
 C.R. Reeves, Modern heuristic techniques, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996b).
 C.R. Reeves, A genetic algorithm for flowshop sequencing, Computers and Operations Research 22 (1995a) 5. CrossRef
 C.R. Reeves, Genetic algorithms and combinatorial optimization, in:Applications of Modern Heuristic Methods, ed. V.J. RaywardSmith (Alfred Waller, HenleyonThames, 1995b).
 C.R. Reeves, Heuristics for scheduling a singlemachine subject to unequal job release times, European Journal of Operational Research 80 (1995c) 397. CrossRef
 C.R. Reeves,Modern Heuristic Techniques for Combinatorial Problems (Blackwell, Oxford, 1993a).
 C.R. Reeves, Improving the efficiency of tabu search for machine sequencing problems, Journal of the Operational Research Society 44 (1993b) 375.
 C.R. Reeves and C. Höln, Integrating local search into genetic algorithms, in:Modern Heuristic Search Methods, ed. V.J. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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).
 C. Rego and C. Roucairol, Using tabu search for solving a dynamic multiterminal truck dispatching problem, European Journal of Operational Research 83 (1995) 411. CrossRef
 R. Rego and C. Roucairol, An efficient implementation of ejection chain procedures for the vehicle routing problem, Working paper RR94/44, Laboratoire PRiSM, Université de Versailles, France (1994).
 J. Renaud, G. Laporte and F.F. Boctor, A tabu search for the multidepot vehicle routing problem, Computers and Operations Research 23 (1996) 229. CrossRef
 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).
 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, NGbackmarking. An algorithm for constraint satisfaction, BT Technology Journal 13 (1995) 102.
 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.
 B. Robic and J. Silc, Algorithm mapping with parallel simulated annealing, Computers and Artificial Intelligence 14 (1995) 339.
 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 hubterminal geometric concepts 2. Baggage and extensions, Journal of Transportation EngineeringASCE 117 (1991) 159.
 A.F. Rocha, Neural nets. A theory for brains and machines,Lecture Notes in Artificial Intelligence 638 (1992) p. 5.
 Y. Rochat and E.D. Taillard, Probabilistic diversification and intensification in local search for vehicle routing, Journal of Heuristics 1 (1995) 147.
 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.
 E. Rolland, Abstract heuristic search methods for graph partitioning, Ph.D. Dissertation, The Ohio State University, Columbus (1991).
 E. Rolland, D.A. Schilling and J.R. Current, An efficient tabu search procedure for the Pmedian problem, Working paper, Graduate School of Management, University of California at Riverside, CA (1993).
 E. Rolland, H. Pirkul and F. Glover, Tabu search for graph partitioning, Annals of Operations Research 63 (1996) 209.
 H.E. Romelin and R.L. Smith, Simulated annealing for constrained global optimization, Journal of Global Optimization 5 (1994) 101. CrossRef
 F. Romeo and A. SangiovanniVincentelli, A theoretical framework for simulated annealing, Algorithmica 6 (1991) 302. CrossRef
 J.S. Rose, W.M. Snelgrove and Z.G. Vranesic, Parallel standard cell placement algorithms with quality equivalent to simulated annealing, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 7 (1988) 387. CrossRef
 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. CrossRef
 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, Errorfree parallel implementation of simulated annealing,Lecture Notes in Computer Science 496 (1991) p. 231.
 P. Rousselragot and G. Dreyfus, A problem independent parallel implementation of simulated annealing. Models and experiments, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 9 (1990) 827. CrossRef
 G. Rudolph, Convergence analysis of canonical genetic algorithms, IEEE Transactions on Neural Networks 5 (1994) 96. CrossRef
 G. Ruppeiner, J.M. Pedersen and P. Salamon, Ensemble approach to simulated annealing, Journal de Physique I 1 (1991) 455. CrossRef
 R.A. Russell, Hybrid heuristics for the vehicle routing problem with time windows, Transportation Science 29 (1995) 156.
 R.A. Rutenbar, Simulated annealing algorithms. An overview, IEEE Circuits and Devices Magazine 5 (1989) 19. CrossRef
 J. Ryan, The depth and width of local minima in discrete solution spaces, Discrete Applied Mathematics 56 (1995) 75. CrossRef
 Y.G. Saab, A fast and robust network bisection algorithm, IEEE Transactions on Computers 44 (1995) 903. CrossRef
 Y.G. Saab and V. Rao, Combinatorial optimization by stochastic evolution, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 10 (1991a) 525. CrossRef
 Y.G. Saab and V. Rao, A stochastic algorithm for circuit bipartitioning,Lecture Notes in Computer Science 507 (1991b) p. 313.
 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).
 N.M. Sadeh and Y. Nakakuki, Focused simulated annealing search. An application to job shop scheduling, Annals of Operations Research 63 (1996) 77.
 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.
 S. Salhi and M. Sari, A heuristic approcah for the multidepot vehicle fleet mix problem, Working paper, School of Mathematics and Statistics, University of Birmingham, England (1995).
 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.
 T. Satake, K. Morikawa and N. Nakamura, Neural network approach for minimizing the makespan of the general job shop, International Journal of Production Economics 33 (1994) 67. CrossRef
 T. Satoh and K. Nara, Maintenance scheduling by using simulated annealing method, IEEE Transactions on Power Systems 6 (1991) 850. CrossRef
 J.E. Savage and M.G. Wloka, Parallelism in graph partitioning, Journal of Parallel and Distributed Computing 13 (1991) 257. CrossRef
 J.D. Schaffer,Proceedings of the Third International Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, 1989).
 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, COGANN92 (1992) p. 1. CrossRef
 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. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 W.T. Scherer and F. Rotman, Combinatorial optimization techniques for spacecraft scheduling automation, Annals of Operations Research 50 (1994) 525. CrossRef
 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).
 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).
 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).
 A. Schober, M. Thuerk and M. Eigen, Optimization by hierarchical mutant production, Biological Cybernetics 69 (1993) 493. CrossRef
 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.
 A.C. Schultz, J.J. Grefenstette and K.A. DeJong, Test and evaluation by genetic algorithms, IEEE ExpertIntelligent Systems and Their Applications 8 (1993) 9.
 L.L. Schumaker, Computing optimal triangulations using simulated annealing, ComputerAided Geometric Design 10 (1993) 329. CrossRef
 H.P. Schwefel,Numerical Optimization for Computer Models (Wiley, Chichester, 1981).
 H.P. Schwefel and R. Männer,Parallel Problem Solving From Nature, PPSN 1 Proceedings, Lecture Notes in Computer Science 496 (Springer, Berlin, 1991).
 R.S. Segall, Some mathematical and computer modeling of neural networks, Applied Mathematical Modelling 19 (1995) 386. CrossRef
 D.A. Sekharan and R.L. Wainwright, Manipulating subpopulations in genetic algorithms for solving theKway 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. CrossRef
 S. Selvakumar and C.S.R. Murthy, An efficient heuristic algorithm for mapping parallel programs onto multicomputers, Microprocessing and Microprogramming 36 (1993) 83. CrossRef
 F. Semet and E.D. Taillard, Solving reallife vehicle routing problems efficiently using tabu search, Annals of Operations Research 41 (1993) 469. CrossRef
 K. Shahookar and P. Mazumder, VLSI cell placement techniques, Computing Surveys 23 (1991) 143. CrossRef
 J.S. Shang, Multicriteria facility layout problem. An integrated approach, European Journal of Operational Research 66 (1993) 291. CrossRef
 B.A. Shapiro and J. Navetta, A massively parallel genetic algorithm for RNA secondary structure prediction, Journal of Supercomputing 8 (1994) 195. CrossRef
 J.A. Shapiro and A.S. Alfa, An experimental analysis of the simulated annealing algorithm for a singlemachine scheduling problem, Engineering Optimization 24 (1995) 79.
 Y.M. Sharaiha, M. Gendreau, G. Laporte and I.H. Osman, A tabu search algorithm for the capacitated minimum spanning tree problem, Working paper, CRT9579, Centre de recherche sur les transports, Montréal (1995).
 R. Sharda, Neural networks for the MS/OR analyst. An application bibliography, Interfaces 24 (1994) 116.
 R. Sharda, Statistical applications of neural networks, Intelligent Systems Report 8 (1991) 12.
 R. Sharda, Neural nets for operations research, Intelligent Systems Report 9 (1992) 14.
 R. Sharda and R. Patil, Connectionist approach to time series prediction. An empirical test, Journal of Intelligent Manufacturing 3 (1992) 317. CrossRef
 P.K. Sharpe, A.G. Chalmers and A. Greenwood, Genetic algorithms for generating minimum path configurations, Microprocessors and Microsystems 19 (1995) 9. CrossRef
 R. Sharpe and B.S. Marksjo, Solution of the facilities layout problem by simulated annealing, Computers Environment and Urban Systems 11 (1986) 147. CrossRef
 G.B. Sheble and K. Brittig, Refined genetic algorithm. Economic dispatch example, IEEE Transactions on Power Systems 10 (1995) 117. CrossRef
 S. Shekhar and M.B. Amin, Generalization by neural networks, IEEE Transactions on Knowledge and Data Engineering 4 (1992) 177. CrossRef
 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. CrossRef
 P.H. Shih and W.S. Feng, An analog neural network approach to global routing problem, Cybernetics and Systems 22 (1991) 747.
 F. Shiratani and K. Yamamoto, Combinatorial optimization by using a neural network operating in blocksequential mode, Systems and Computers in Japan 25 (1994) 103.
 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.
 A. Silver, R.V.V. Vidal and D. de Werra, A tutorial on heuristic methods, European Journal of Operational Research 5 (1980) 153. CrossRef
 P.D. Simic, Statistical mechanics as the underlying theory of the elastic and neural optimizations, IEEE Transactions on Neural Networks 1 (1990) 89.
 M.W. Simmen, Parameter sensitivity of the elastic net approach to the travelling salesman problem, Neural Computation 3 (1991) 363.
 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.
 M. Sinclair, Comparison of the performance of modern heuristics for combinatorial optimization on real data, Computers and Operations Research 20 (1993) 687. CrossRef
 G.S. Singh and K.R. Deshpande, On fast load partitioning by simulated annealing and heuristic algorithms for generalclass of problems, Advances in Engineering Software 16 (1993) 23. CrossRef
 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.
 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. CrossRef
 J. SkorinKapov, Extensions of a tabu search adaptation to the quadratic assignment problem, Computers and Operations Research 21 (1994) 855. CrossRef
 J. SkorinKapov, Tabu search applied to the quadratic assignment problem, ORSA Journal on Computing 2 (1990) 33.
 J. SkorinKapov and J.F. Labourdette, On minimum congestion routing in rearrangeable multihop lightwave networks, Journal of Heuristics 1 (1995) 129.
 D. SkorinKapov and J. SkorinKapov, On tabu search for the location of interacting hub facilities, European Journal of Operational Research 73 (1994) 502. CrossRef
 J. SkorinKapov and A.J. Vakharia, Scheduling a flowline manufacturing cell. A tabu search approach, International Journal of Production Research 31 (1993) 1721.
 R. Slowinski, B. Soniewicki and J. Weglarz, DSS for multiobjective project scheduling, European Journal of Operational Research 79 (1994) 220. CrossRef
 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).
 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.
 J. Smith and T.C. Fogarty, An adaptive polyparental 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).
 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).
 S. Sofianopoulou, A queueing network application to a telecommunications distributed system, RAIRO — Operations Research 26 (1992a) 409.
 S. Sofianopoulou, Simulated annealing applied to the process allocatio problem, European Journal of Operational Research 60 (1992b) 327. CrossRef
 A. Sohn, Generalized speculative computation of parallel simulated annealing, Annals of Operations Research 63 (1996) 29.
 A. Sohn, ParallelNary speculative computation of simulated annealing, IEEE Transactions on Parallel and Distributed Systems 6 (1995) 997. CrossRef
 L. Sondergeld and S. Voß, A starshaped diversification approach in tabu search, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 L. Song and A. Vannelli, A VLSI placement method using tabu search, Microelectronics Journal 23 (1992) 167. CrossRef
 P. Soriano and M. Gendreau, Diversification strategies in tabu search algorithm for the maximum clique problem, Annals of Operations Research 63 (1996) 189.
 G.B. Sorkin, Efficient simulated annealing on fractal energy landscapes, Algorithmica 6 (1991) 367. CrossRef
 A. Souilah, Simulated annealing for manufacturing systems layout design, European Journal of Operational Research 82 (1995) 592. CrossRef
 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. CrossRef
 W.M. Spears and V. Anand, A study of crossover operators in genetic programming,Lecture Notes in Artificial Intelligence 542 (1991) p. 409.
 R. Srichander, Efficient schedules for simulated annealing, Engineering Optimization 24 (1995) 161.
 J. Sridhar and C. Rajendran, Scheduling in a cellular manufacturing system. A simulated annealing approach, International Journal of Production Research 31 (1993) 2927.
 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. CrossRef
 M. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation 2 (1995) 221.
 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.
 M. Srinivas and L.M. Patnaik, Genetic algorithms. A survey, Computer 27 (1994b) 17. CrossRef
 B. Srivastava and W.H. Chen, Part type selection problem in flexible manufacturing systems: Tabu search algorithms, Annals of Operations Research 41 (1993) 279. CrossRef
 P.F. Stadler, Correlation in landscapes of combinatorial optimization problems, Europhysics Letters 20 (1992) 479.
 J. Stander and B.W. Silverman, Temperature schedules for simulated annealing, Statistics and Computing 4 (1994) 21. CrossRef
 J.P.P. Starink and E. Backer, Finding point correspondences using simulated annealing, Pattern Recognition 28 (1995) 231. CrossRef
 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).
 T. Starkweather, D. Whitley and K. Mathias, Optimization using distributed genetic algorithms,Lecture Notes in Computer Science 496 (1991) p. 165.
 J. Stender,Parallel Genetic Algorithms. Theory and Applications (ISO Press, Amsterdam, 1992).
 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.
 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).
 G.S. Stiles, The effect of numerical precision upon simulated annealing, Physics Letters A 185 (1994) 253. CrossRef
 D.J. Stockton and L. Quinn, Aggregate production planning using genetic algorithms, Journal of Engineering Manufacture 209 (1995) 201.
 R.H. Storer, S.W. Flanders and S.D. Wu, Problem space local search for number partitioning, Annals of Operations Research 63 (1996) 465.
 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.
 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.
 P.N. Strenski and S. Kirkpatrick, Analysis of finite length annealing schedules, Algorithmica 6 (1991) 346. CrossRef
 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.
 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.
 P.N. Suganthan, E.K. Teoh and D.P. Mital, Selforganizing Hopfield network for attributed relational graph matching, Image and Vision Computing 13 (1995) 61. CrossRef
 D.K. Sun, R. Batta and L. Lin, Effective job shop scheduling through active chain manipulation, Computers and Operations Research 22 (1995) 159. CrossRef
 D.K. Sun, L. Lin and R. Batta, Cellformation using tabu search, Computers and Industrial Engineering 28 (1995) 485. CrossRef
 L.X. Sun, F. Xu, Y.Z. Liang, Y.L. Xie and R.Q. Yu, Cluster analysis by thekmeans algorithm and simulated annealing, Chemometrics and Intelligent Laboratory Systems 25 (1994) 51. CrossRef
 L.X. Sun, Y.I. Xie, X.H. Song, J.H. Wang and R.Q. Yu, Clusteranalysis by simulated annealing, Computers and Chemistry 18 (1994) 103. CrossRef
 M. Sun and P.G. McKeown, Tabu search applied to the general fixed charge problem, Annals of Operations Research 41 (1993) 405. CrossRef
 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.
 W.J. Sun and C. Sechen, Efficient and effective placement for very large circuits, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 14 (1995) 349. CrossRef
 G. Suresh and S. Sahu, Stochastic assembly line balancing using simulated annealing, International Journal of Production Research 32 (1994) 2249.
 G. Suresh and S. Sahu, Multiobjective facility layout using simulated annealing, International Journal of Production Economics 32 (1993) 239. CrossRef
 P.D. Surry, N.J. Radcliffe and I.D. Boyd, A multiobjective 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. CrossRef
 J. Suzuki, A Markov chain analysis on simple genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 655.
 G. Syswerda, Schedule optimization using genetic algorithms, in:Handbook of Genetic Algorithms, ed. L. Davis (Van Nostrand Reinhold, New York, 1991).
 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.
 H. Szu and S. Foo, Divideandconquer orthogonality principle for parallel optimizations in TSP, Neurocomputing 8 (1995) 249. CrossRef
 H. Szu and R. Hartley, Fast simulated annealing, Physics Letters A122 (1987) 157.
 S. Szykman and J. Cagan, A simulated annealing based approach to 3dimensional component packing, Journal of Mechanical Design 117 (1995) 308.
 R. Tadei, F. DellaCroce and G. Menga, Advanced search techniques for the jobshop problem. A comparison, RAIRO — Operations Research 29 (1995) 179.
 E.D. Taillard, Comparison of iterative searches for the quadratic assignment problem, Location Science 3 (1995) 87. CrossRef
 E.D. Taillard, Parallel taboo search techniques for the job shop scheduling problem, ORSA Journal on Computing 6 (1994) 108.
 E.D. Taillard, Parallel iterative search methods for vehicle routing problems, Networks 23 (1993a) 661.
 E.D. Taillard, Recherches iterativés dirigées parallèles, Ph.D. Dissertation, Department of Mathématiques, Ecole Polytechnique de Lausanne, Switzerland (1993b).
 E.D. Taillard, Robust taboo search for the quadratic assignment problem, Parallel Computing 17 (1991) 443. CrossRef
 T. Takada, K. Sanou and S. Fukumara, A neuralnetwork system for solving an assortment problem in the steel industry, Annals of Operations Research 57 (1995) 265. CrossRef
 Y. Takefuji and K.C. Lee, A nearoptimum parallel planarization algorithm, Science 245 (1989) 1221.
 Y. Takefuji and J. Wang,Neural Networks for Optimization and Combinatorics (World Scientific, Singapore, 1994).
 K.Y. Tam, A simulated annealing algorithm for allocating space to manufacturing cells, International Journal of Production Research 30 (1992a) 63.
 K.Y. Tam, Genetic algorithms, function optimization, and facility layout design, European Journal of Operational Research 63 (1992b) 322. CrossRef
 K.Y. Tam and M.Y. Kiang, Managerial applications of neural networks. The case of bank failure predictions, Management Science 38 (1992c) 926.
 H. Tamaki, M. Mori, M. Araki, Y. Mishima and H. Ogai, Multicriteria optimization by genetic algorithms. A case of scheduling in hot rolling process, in:Proceedings of the 3rd Conference of the Association of AsianPacific Operational Research Societies within IFORS (APORS'94, 1994) p. 374.
 G. Tambouratzis, Optimizing the clustering performance of a selforganizing logic neuralnetwork with topologypreserving capabilities, Pattern Recognition Letters 15 (1994) 1019. CrossRef
 T. Tanaka, T. Higuch and T. Furuya, An efficient algorithm for solving optimization problems on Hopfieldtype neural networks, Systems and Computers in Japan 26 (1995) 73.
 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. CrossRef
 M. Tandon, P.T. Cummings and M.D. Levan, Flowshop sequencing with nonpermutation schedules, Computers and Chemical Engineering 15 (1991) 601. CrossRef
 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.
 R. Tanese, Distributed genetic algorithms for function optimization, Ph.D. Dissertation, The University of Michigan, Ann Arbor (1989).
 L.X. Tao and Y.C. Zhao, Effective heuristic algorithms for VLSI circuit partition, IEE Proceedings — G: Circuits, Devices and Systems 140 (1993a) 127.
 L.X. Tao and Y.C. Zhao, Multiway graph partition by stochastic probe, Computers and Operations Research 20 (1993b) 321. CrossRef
 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.
 D.M. Tate and A.E. Smith, A genetic approach to the quadratic assignment problem, Computers and Operations Research 22 (1995) 73. CrossRef
 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.
 A. Taudes and T. Netousek, Implementing branch and bound algorithms on a cluster of workstations. A survey, some new results and open problems,Lecture Notes in Economics and Mathematical Systems 367 (1991) p. 79.
 J.G. Taylor,Neural Networks (Alfred Waller, HenleyonThames, 1995).
 J.G. Taylor,Mathematical Approaches to Neural Networks (NorthHolland, Amsterdam, 1993).
 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).
 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.
 P.B. Thanedar and G.N. Vanderplaats, Survey of discrete variable optimization for structural design, Journal of Structural EngineeringASCE 121 (1995) 301. CrossRef
 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).
 S.R. Thangiah and A.V. Gubbi, Effect of genetic sectoring on vehicle routing problems with time windows, Working paper, SRUCpScTR9213, Computer Science Department, Slippery Rock University, PA (1992).
 S.R. Thangiah and K.E. Nygard, MICAH. A genetic algorithm system for multicommodity transshipment problems,Proccedings of the 8th IEEE Conference on Artificial Intelligence for Applications, Monterey, CA (1992a) p. 240.
 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.
 S.R. Thangiah, K.E. Nygard, Dynamic trajectory routing using an adaptive search strategy, Working paper SRUCpScTR9220, Computer Science Department, Slippery Rock University, PA (1992c).
 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).
 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.
 V.E. Theodoracatos and J.L. Grimsley, The optimal packing of arbitrarilyshaped polygons using simulated annealing and polynomialtime cooling schedules, Computer Methods in Applied Mechanics and Engineering 125 (1995) 53. CrossRef
 J. Thiel and S. Voß, Some experiences on solving multiconstraint zeroone knapsack problems with genetic algorithms, INFOR 32 (1994) 226.
 G.M. Thompson, A simulated annealing heuristic for shift scheduling using noncontinuously available employees, Computers and Operations Research 23 (1996) 275. CrossRef
 J.M. Thompson and K.A. Dowsland, Variants of simulated annealing for the examination timetabling problem, Annals of Operations Research 63 (1996) XXX
 K.W. Tindell, A. Burns and A.J. Wellings, Allocating hard realtime tasks. An NPhard problem made easy, RealTime Systems 4 (1992) 145. CrossRef
 V. Todorov, Computing the minimum covariance determinant estimator by simulated annealing, Computational Statistics and Data Analysis 14 (1992) 515. CrossRef
 P. Toth and D. Vigo, Heuristic algorithms for the handicapped persons for transportation problem, Working paper DEISOR957, D.E.I.S., Università di Bologna, Italy (1995).
 M. Toulouse, T.G. Crainic and M. Gendreau, Communication issues in designing cooperative multithread parallel searches, in:Metaheuristics. Theory and Applications, ed. I.H. Osman and J.P. Kelly (Kluwer, Boston, 1996).
 G.G. Towell and J.W. Shavlik, Knowledgebased artificial neural networks, Artificial Intelligence 70 (1994) 119. CrossRef
 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.
 A. Trouve, Asymptotical behavior of several interacting annealing processes, Probability Theory and Related Fields 102 (1995) 123. CrossRef
 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.
 A. Trouve, Optimal convergence rate for generalized simulated annealing, Comptes Rendus de l'Academie des Sciences Série I — Mathématique 315 (1992b) 1197.
 E.P.K. Tsang,Foundations of Constraint Satisfaction (Academic Press, London, 1993).
 E.P.K. Tsang, Scheduling techniques. A comparative study, BT Technology Journal 13 (1995) 16.
 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.
 J.N. Tsitsiklis, Markov chains with rare transitions and simulated annealing, Mathematics of Operations Research 14 (1989) 70.
 Y. Tsujimura, M. Gen and E. Kubota, Solving fuzzy assemblyline balancing problem with genetic algorithms, Computers and Industrial Engineering 29 (1995) 543. CrossRef
 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. CrossRef
 B.C.H. Turton, Optimization of genetic algorithms using the Taguchi method, Journal of Systems Engineering 4 (1994) 121.
 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. CrossRef
 G.J. Udo and Y.P. Gupta, Applications of neural networks in manufacturing managementsystems, Production Planning and Control 5 (1994) 258.
 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.
 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.
 K. Urahama, Analog method for solving combinatorial optimization problems, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E77A (1994) 302.
 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. CrossRef
 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 (NorthHolland, Amsterdam, 1992).
 S. Vaithyanathan and J.P. Ignizio, A stochastic neural network for resource constrained scheduling, Computers and Operations Research 19 (1992) 241. CrossRef
 A.J. Vakharia and Y.L. Chang, A simulated annealing approach to scheduling a manufacturing cell, Naval Research Logistics 37 (1990) 559.
 A.I. Vakhutinsky and B.L. Golden, A hierarchical strategy for solving traveling salesman problem using elastic nets, Journal of Heuristics 1 (1995) 67.
 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.
 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).
 A. van Breedam, An analysis of the behavior of heuristics for the vehicle routing problem for selection of problems with vehiclerelated, customerrelated and timerelated constraints, Ph.D. Dissertation, Faculty of Applied Economics, University of Antwerp, Belgium (1994).
 A. van Breedam, Improvement heuristics for the vehicle routing problem based on simulated annealing, European Journal of Operational Research 86 (1995) 480. CrossRef
 D.E. van de Bout and T.K. Miller, Graph partitioning using annealed neural networks, IEEE Transactions on Neural Networks 1 (1990) 192. CrossRef
 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.
 L.J.J. van Derbruggen, J.K. Lenstra and P.C. Schuur, Variabledepth search for the singlevehicle pickup and delivery problem with time windows, Transportation Science 27 (1993) 298.
 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 (NorthHolland, Amsterdam, 1992).
 P.J.M. van Laarhoven, E.H.L. Aarts and J.K. Lenstra, Job shop scheduling by simulated annealing, Operations Research 40 (1992) 113.
 P.J.M. van Laarhoven and E.H.L. Aarts,Simulated Annealing. Theory and Applications (Reidel, Dordrecht, 1987).
 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 (NorthHolland, Amsterdam, 1991).
 P. Vanbommel, T. Vanderweide and C.B. Lucasius, Genetic algorithms for optimal logical database design, Information and Software Technology 36 (1994) 725. CrossRef
 J. Vancza and A. Markus, Genetic algorithms in process planning, Computers in Industry 17 (1991) 181. CrossRef
 P. Vanhentenryck, H. Simonis and M. Dincbas, Constraint satisfaction using constraint logic programming, Artificial Intelligence 58 (1992) 113. CrossRef
 H. Vanhove and A. Verschoren, Genetic algorithms and trees 1. Recognition trees (the fixedwidth case), Computers and Artificial Intelligence 13 (1994) 453.
 M.M. Vanhulle and G.A. Orban, Representation and processing in a stochastic neural network. An integrated approach, Neural Networks 4 (1991) 643. CrossRef
 M. VazPato 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).
 V.S. Vempati, C.L. Chen and S.F. Bullington, An effective heuristic for flowshop problems with total flow time as criterion, Computers and Industrial Engineering 25 (1993) 219. CrossRef
 R. Vemuri and R. Vemuri, Genetic algorithm for MCM partitioning, Electronics Letters 30 (1994a) 1270. CrossRef
 R. Vemuri and R. Vemuri, MCM layer assignment using genetic search, Electronics Letters 30 (1994b) 1635. CrossRef
 G. Venkataraman and G. Athithan, Spinglass, the traveling salesman problem, neural networks and all that, Pramana — Journal of Physics 36 (1991) 1.
 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. CrossRef
 V. Venugopal and T.T. Narendran, Cellformation in manufacturing systems through simulated annealing. An experimental evaluation, European Journal of Operational Research 63 (1992b) 409. CrossRef
 M.G.A. Verhoeven, Parallel local search, Ph.D. Dissertation, Eindhoven University of Technology, The Netherlands (1996).
 M.G.A. Verhoeven and E.H.L. Aarts, Parallel local search, Journal of Heuristics 1 (1995) 43.
 M.G.A. Verhoeven, E.H.L. Aarts and P.C.J. Swinkels, A parallel 2opt algorithm for the travelingsalesman problem, Future Generation Computer Systems 11 (1995) 175. CrossRef
 F.J. Vico and F. Sandoval, Use of genetic algorithms in neural networks definition,Lecture Notes in Computer Science 540 (1991) p. 196.
 R.V.V. Vidal,Applied Simulated Annealing, Lecture Notes in Economics and Mathematical Systems 396 (Springer, Berlin, 1993).
 G.A. Vignaux and Z. Michalewicz, A genetic algorithm for the linear transportation problem, IEEE Transactions of Systems, Man and Cybernetics 21 (1991) 445.
 H.P. Voigt, Soft genetic operators in evolutionary algorithms,Lecture Notes in Computer Science 899 (1995) p. 123.
 G. von Laszewski and H. Mühlenbein, Partitioning a graph with a parallel genetic algorithm,Lecture Notes in Computer Science 496 (1991) p. 165.
 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. CrossRef
 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. RaywardSmith, I.H. Osman, C.R. Reeves and G.D. Smith (Wiley, Chichester, 1996).
 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).
 S. Voß, Tabu search. Applications and prospects, in:Network Optimization Problems, ed. D.Z. Du and P.M. Pardalos (World Scientific, Singapore, 1993a).
 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).
 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.
 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.
 G.A. Walters and T. Lohbeck, Optimal layout of tree networks using genetic algorithms, Engineering Optimization 22 (1993) 27.
 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. CrossRef
 J. Wang, Artificial neural networks versus natural neural networks. A connectionist paradigm for preference assessment, Decision Support Systems 11 (1994b) 415. CrossRef
 J. Wang, Multipleobjective optimization of machining operations based on neural networks, International Journal of Advanced Manufacturing Technology 8 (1993a) 235. CrossRef
 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. CrossRef
 J. Wang, Analog neural network for solving the assignment problem, Electronics Letters 28 (1992a) 1047.
 J. Wang, Recurrent neural networks for solving quadratic programming problems with equality constraints, Electronics Letters 28 (1992b) 1345.
 J. Wang, On the asymptotic properties of recurrent neural networks for optimization, International Journal of Pattern Recognition and Artificial Intelligence 5 (1991) 581. CrossRef
 J. Wang and V. Chankong, Recurrent neural networks for linear programming. Analysis and design principles, Computers and Operations Research 19 (1992) 297. CrossRef
 J. Wang and V. Chankong, Neurallyinspired stochastic algorithm for determining multistage multiattribute acceptance sampling inspection plans, Journal of Intelligent Manufacturing 2 (1991) 327. CrossRef
 J. Wang and H. Li, Solving simultaneous linear equations using recurrent neural networks, Information Sciences 76 (1993) 255. CrossRef
 J. Wang and B. Malakooti, Characterization of training errors in supervised learning using gradientbased learning rules, Neural Networks 6 (1993) 1073.
 J. Wang and B. Malakooti, A feedforward and neural network for multiple criteria decision making, Computers and Operations Research 19 (1992) 151. CrossRef
 J. Wang and Y. Takefuji,Neural Networks in Design and Manufacturing (World Scientific, Singapore, 1993).
 J. Wang, J. Yang and H. Lee, Multicriteria order acceptance decision support in overdemanded job shops. A neural network approach, Mathematical and Computer Modeling 19 (1994) 1. CrossRef
 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. CrossRef
 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. CrossRef
 S.H. Wang and N.P. Archer, A neuralnetwork technique in modeling multiple criteria multiple person decisionmaking, Computers and Operations Research 21 (1994) 127. CrossRef
 X.D. Wang and T. Chen, Performance and area optimization of VLSI systems using genetic algorithms, VLSI Design 3 (1995) 43.
 G.S. Wasserman and A. Sudjianto, All subsets regression using a genetic search algorithm, Computers and Industrial Engineering 27 (1994) 489. CrossRef
 T. Watanabe, Y. Hashimoto, I. Nishikawa and H. Tokumaru, Line balancing using a genetic evolution model, Control Engineering Practice 3 (1995) 69. CrossRef
 C. WenChyuan, G.J. Gutierrez and P. Kouvelis, Simulated annealing and tabu search, in:Intelligent Design and Manufacturing, ed. A. Kusiak (Wiley, Chichester, 1992).
 F. Werner, On the heuristic solution of the permutation flow shop problem by path algorithms, Computers and Operations Research 20 (1993) 707. CrossRef
 D. Whitley, A review of models for simple and cellular genetic algorithms in:Applications of Modern Heuristic Methods, ed. V. RaywardSmith (Alfred Waller, HenleyonThames, 1995).
 D. Whitley, A genetic algorithm tutorial, Statistics and Computing 4 (1994) 65. CrossRef
 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.
 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.
 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).
 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).
 D. Whitley and T. Starkweather, GENITOR II. A distributed genetic algorithm, Journal of Experimental and Theoretical Artificial Intelligence 2 (1990) 189.
 D. Whitley, T. Starkweather and C. Bogart, Genetic algorithms and neural networks. Optimizing connections and connectivity, Computing 14 (1990) 347.
 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.
 M. Widmer, Job shop scheduling with tooling constraints. A tabu search approach, Journal of the Operational Research Society 42 (1991) 75.
 M. Widmer and A. Hertz, A new heuristic method for the flow shop sequencing problem, European Journal of Operational Research 41 (1989) 186. CrossRef
 M.R. Wilhelm and T.I. Ward, Solving quadratic assignment problems by simulated annealing, IIE Transactions 19 (1987) 107.
 T.M. Willems and J.E. Rooda, Neural networks for jobshop scheduling, Control Engineering Practice 2 (1994) 31. CrossRef
 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.
 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.
 G. Wilson and C. Pawley, On the stability of the TSP algorithm of Hopfield and Tank, Biological Cybernetics 58 (1988) 63. CrossRef
 J.M. Wilson, A genetic algorithm for the generalized assignment problem, Working paper, Business School, Loughborough University, England (1995).
 R.I. Wilson, Ranking college football teams. A neuralnetwork approach, Interfaces 25 (1995) 44.
 R. Wilson and R. Sharda, Bankruptcy prediction using neural networks, Decision Support Systems 11 (1994) 545. CrossRef
 S.S. Wilson, Teaching network connectivity using simulated annealing on a massively parallel processor, Proceedings of the IEEE 79 (1991) 559. CrossRef
 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. CrossRef
 E. Wong, Stochastic neural networks, Algorithmica 6 (1991) 466. CrossRef
 K.P. Wong and C.C. Fung, Simulated annealing based economic dispatch algorithm, IEEE Proceedings — C Generation Transmission and Distribution 140 (1993) 509.
 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. CrossRef
 W.S. Wong, Matrix representation and gradient flows for NPhard problems, Journal of Optimization Theory and Applications 87 (1995) 197.
 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).
 D.L. Woodruff, Ghost image processing for minimum covariance determinants, ORSA Journal on Computing 7 (1995) 463.
 D.L. Woodruff, Simulated annealing and tabu search. Lessons from a line search, Computers and Operations Research 21 (1994) 823. CrossRef
 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.
 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.
 D.L. Woodruff and E. Zemel, Hashing vectors for tabu search, Annals of Operations Research 41 (1993) 123. CrossRef
 A. Wren and D.O. Wren, A genetic algorithm for public transport driver scheduling, Computers and Operations Research 22 (1995) 101. CrossRef
 M.B. Wright, Timetabling county cricket fixtures using a form of tabu search, Journal of the Operational Research Society 45 (1994) 758.
 M.B. Wright, A fair allocation of county cricket opponents, Journal of the Operational Research Society 43 (1992) 195.
 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).
 S.J. Wu and P.T. Chow, Genetic algorithms for nonlinear mixed discreteinteger optimization problems via metagenetic parameter optimization, Engineering Optimization 24 (1995a) 137.
 S.J. Wu and P.T. Chow, Steadystate genetic algorithms for discrete optimization of trusses, Computers and Structures 56 (1995b) 979. CrossRef
 Y. Wu and R.L. Wainwright, Nearoptimal 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. CrossRef
 Q. Xia and S. Macchietto, Routing, scheduling and product mix optimization by minimax algebra models, Chemical Engineering Research and Design 72 (1994) 408.
 Y.S. Xia and J.S. Wang, Neuralnetwork for solving linearprogramming problems with bounded variables, IEEE Transactions on Neural Networks 6 (1995) 515. CrossRef
 Y. Xin, Simulated annealing with extended neighborhood, International Journal of Computer Mathematics 40 (1991) 169.
 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.
 J. Xu, List of interesting optimization codes in public domain, Working paper, Graduate School of Business, University of Colorado, Boulder (1995).
 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).
 L. Xu and E. Oja, Improved simulated annealing, Boltzmann machine, and attributed graph matching,Lecture Notes in Computer Science 412 (1990) p. 151.
 X. Xu and W.T. Tsai, Effective neural algorithms for the traveling salesman problem, Neural Networks 4 (1991) 193. CrossRef
 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).
 T. Yamada and R. Nakano, Jobshop 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).
 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.
 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.
 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. CrossRef
 T.Y. Yang, Z.S. He and K.K. Cho, An effective heuristic method for generalized jobshop scheduling with duedates, Computers and Industrial Engineering 26 (1994) 647. CrossRef
 X.F. Yang and M. Gen, Evolution program for bicriteria transportation problem, Computers and Industrial Engineering 27 (1994) 481. CrossRef
 M. Yannakakis, The analysis of local search problems and their heuristics,Lecture Notes in Computer Science 415 (1990) p. 298.
 X. Yao, Call routing by simulated annealing, International Journal of Electronics 79 (1995) 379.
 X. Yao, A review of evolutionary artificial neural networks, International Journal of Intelligent Systems 8 (1993a) 539.
 X. Yao, An empirical study of genetic operators in genetic algorithms, Microprocessing and Microprogramming 38 (1993b) 707. CrossRef
 X. Yao, Finding approximate solutions to NPhard problems by neural networks is hard, Information Procession Letters 41 (1992) 93. CrossRef
 C.W. Yeh, C.K. Cheng and T.T.Y. Lin, Optimization by iterative improvement. An experimental evaluation on 2way partitioning, IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems 14 (1995) 145. CrossRef
 I.C. Yeh, Construction site layout using annealed neuralnetwork, Journal of Computing in Civil Engineering 9 (1995) 201. CrossRef
 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. CrossRef
 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.
 L. Yong, K. Lishan and D.J. Evans, The annealing evolution algorithm as function optimizer, Parallel Computing 21 (1995) 389. CrossRef
 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. CrossRef
 Y.O. Yoon, G. Swales and T.M. Margavio, A comparison of discriminantanalysis versus artificial neural networks, Journal of the Operational Research Society 44 (1993) 51.
 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.
 A.L. Yuille, Generalized deformable models, statistical physics and matching problems, Neural Comptutation 2 (1990) 1.
 J.M. Yunker and J.D. Tew, Simulation optimization by genetic search, Mathematics and Computers in Simulation 37 (1994) 17. CrossRef
 D. Yuret, From genetic algorithms to efficient optimization, M.Sc. Dissertation, Department of Electrical Engineering and Computer Science, M.I.T., Cambridge, MA (1994).
 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).
 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).
 S. Zanakis, J. Evans and A. Vazacopoulos, Heuristic methods and applications: A categorized survey, European Journal of Operational Research 43 (1989) 88. CrossRef
 M.R. Zargham, A simulated annealing multilayer router, Integration — The VLSI Journal 13 (1992) 179. CrossRef
 S.H. Zegordi, K. Itoh and T. Enkawa, Knowledgable simulated annealing scheme for the early tardy flowshop scheduling problem, International Journal of Production Research 33 (1995a) 1449.
 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. CrossRef
 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.
 S.W. Zhang, X. Zhu and L.H. Zou, Second order neural nets for constrained optimization, IEEE Transactions on Neural Networks 3 (1992) 1021. CrossRef
 D. Zhu and R. Padman, Neural networks for heuristics selection. An application in resourceconstrained 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).
 V. Zissimopoulos, On the performance guarantee of neural networks for NPhard optimization problems, Information Processing Letters 54 (1995) 317. CrossRef
 P.J. Zwietering, E.H. Aarts and J. Wessels, Exact classification with twolayered perceptions, International Journal of Neural Systems 3 (1992) 143. CrossRef
 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. CrossRef
 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.
 Title
 Metaheuristics: A bibliography
 Journal

Annals of Operations Research
Volume 63, Issue 5 , pp 511623
 Cover Date
 19961001
 DOI
 10.1007/BF02125421
 Print ISSN
 02545330
 Online ISSN
 15729338
 Publisher
 Baltzer Science Publishers, Baarn/Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 Artificial intelligence
 bibliography
 combinatorial optimization
 constraint logic programming
 evolutionary computation
 genetic algorithms
 greedy random adaptive search procedure
 heuristics
 hybrids
 local search
 metaheuristics
 neural networks
 nonmonotonic search strategies
 problemspace method
 simulated annealing
 tabu search
 threshold algorithms
 Industry Sectors
 Authors

 Ibrahim H. Osman ^{(1)}
 Gilbert Laporte ^{(2)}
 Author Affiliations

 1. Institute of Mathematics and Statistics, University of Kent, CT2 7NF, Canterbury, Kent, UK
 2. Centre de recherche sur les transports, Université de Montréal, Succursale Centreville, Case postable 6128, H3C 3J7, Montréal, Canada