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A Gentle Introduction to Memetic Algorithms

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 57))

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Bibliography

  1. H.S. Abdinnour (1998) A hybrid heuristic for the uncapacitated hub location problem. European Journal of Operational Research, 106(2–3), 489–99.

    Google Scholar 

  2. C.C. Aggarwal, J.B. Orlin and R.P. Tai (1997) Optimized crossover for the independent set problem. Operations Research, 45(2), 226–234.

    MathSciNet  Google Scholar 

  3. J. Aguilar and A. Colmenares (1998) Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Analysis and Applications, 1(1), 52–61.

    Article  Google Scholar 

  4. D. Aldous and U. Vazirani (1994) “Go with the winners” algorithms. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science. IEEE, Los Alamitos, CA, pp. 492–501.

    Google Scholar 

  5. A. Augugliaro, L. Dusonchet and E. Riva-Sanseverino (1998) Service restoration in compensated distribution networks using a hybrid genetic algorithm. Electric Power Systems Research, 46(1), 59–66.

    Article  Google Scholar 

  6. R. Axelrod and W.D. Hamilton (1981) The evolution of cooperation. Science, 211(4489), 1390–1396.

    MathSciNet  Google Scholar 

  7. K. Aygun, D.S. Weile and E. Michielssen (1997) Design of multilayered periodic strip gratings by genetic algorithms. Microwave and Optical Technology Letters, 14(2), 81–85.

    Article  Google Scholar 

  8. T. Bäck and F. Hoffmeister (1991) Adaptive search by evolutionary algorithms. In: W. Ebeling, M. Peschel and W. Weidlich (eds.), Models of Selforganization in Complex Systems, number 64 in Mathematical Research, Akademie-Verlag, pp. 17–21.

    Google Scholar 

  9. Th. Bäck (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.

    Google Scholar 

  10. M.J. Bayley, G. Jones, P. Willett and M.P. Williamson (1998) Genfold: A genetic algorithm for folding protein structures using NMR restraints. Protein Science, 7(2), 491–499.

    Google Scholar 

  11. J. Beasley and P.C. Chu (1996) A genetic algorithm for the set covering problem. European Journal of Operational Research, 94(2), 393–404.

    Article  Google Scholar 

  12. J. Beasley and P.C. Chu (1998) A genetic algorithm for the multidimensional knapsack problem. Journal of Heuristics, 4, 63–86.

    Google Scholar 

  13. B. Becker and R. Drechsler (1994) Ofdd based minimization of fixed polarity Reed-Muller expressions using hybrid genetic algorithms. In: Proceedings IEEE International Conference on Computer Design: VLSI in Computers and Processor. IEEE, Los Alamitos, CA, pp. 106–110.

    Google Scholar 

  14. R. Berretta and P. Moscato (1999) The number partitioning problem: An open challenge for evolutionary computation? In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 261–278.

    Google Scholar 

  15. K.D. Boese (1995) Cost versus Distance in the Traveling Salesman Problem. Technical Report TR-950018, UCLA CS Department.

    Google Scholar 

  16. A.H.W. Bos (1998) Aircraft conceptual design by genetic/gradient-guided optimization. Engineering Applications of Artificial Intelligence, 11(3), 377–382.

    Article  Google Scholar 

  17. D. Brown, C. Huntley and A. Spillane (1989) A parallel genetic heuristic for the quadratic assignment problem. In: J. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, pp. 406–415.

    Google Scholar 

  18. T.N. Bui and B.R. Moon (1996) Genetic algorithm and graph partitioning. IEEE Transactions on Computers, 45(7), 841–855.

    MathSciNet  Google Scholar 

  19. T.N. Bui and B.R. Moon (1998) GRCA: A hybrid genetic algorithm for circuit ratio-cut partitioning. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(3), 193–204.

    Google Scholar 

  20. E.K. Burke, D.G. Elliman and R.F. Weare (1995) A hybrid genetic algorithm for highly constrained timetabling problems. In: Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA, pp. 605–610.

    Google Scholar 

  21. E.K. Burke, K.S. Jackson, J.H. Kingston and R.F. Weare (1997) Automated timetabling: The state of the art. The Computer Journal, 40(9), 565–571.

    Article  Google Scholar 

  22. E.K. Burke and J.P. Newall (1997) A phased evolutionary approach for the timetable problem: An initial study. In: Proceedings of the ICONIP/ANZIIS/ANNES’ 97 Conference. Springer-Verlag, Dunedin, New Zealand, pp. 1038–1041.

    Google Scholar 

  23. E.K. Burke, J.P. Newall and R.F. Weare (1996) A memetic algorithm for university exam timetabling. In: E.K. Burke and P. Ross (eds.), The Practice and Theory of Automated Timetabling, volume 1153 of Lecture Notes in Computer Science. Springer-Verlag, pp. 241–250.

    Google Scholar 

  24. E.K. Burke, J.P. Newall and R.F. Weare (1998) Initialisation strategies and diversity in evolutionary timetabling. Evolutionary Computation, 6(1), 81–103.

    Google Scholar 

  25. E.K. Burke and A.J. Smith (1997) A memetic algorithm for the maintenance scheduling problem. In: Proceedings of the ICONIP/ANZIIS/ANNES’ 97 Conference. Springer-Verlag, Dunedin, New Zealand, pp. 469-472.

    Google Scholar 

  26. E.K. Burke and A.J. Smith (1999) A memetic algorithm to schedule grid maintenance. In: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation, Vienna: Evolutionary Computation and Fuzzy Logic for Intelligent Control, Knowledge Acquisition and Information Retrieval. IOS Press 1999, pp. 122–127.

    Google Scholar 

  27. E.K. Burke and A.J. Smith (1999) A multi-stage approach for the thermal generator maintenance scheduling problem. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 1085–1092.

    Google Scholar 

  28. S. Cadieux, N. Tanizaki and T. Okamura (1997) Time efficient and robust 3-D brain image centering and realignment using hybrid genetic algorithm. In: Proceedings of the 36th SICE Annual Conference. IEEE, pp. 1279–1284.

    Google Scholar 

  29. J. Carrizo, F.G. Tinetti and P. Moscato (1992) A computational ecology for the quadratic assignment problem. In: Proceedings of the 21st Meeting on Informatics and Operations Research. Buenos Aires, SADIO.

    Google Scholar 

  30. S. Cavalieri and P. Gaiardelli (1998) Hybrid genetic algorithms for a multiple-objective scheduling problem. Journal of Intelligent Manufacturing, 9(4), 361–367.

    Article  Google Scholar 

  31. N. Chaiyaratana and A.M.S. Zalzala (1999) Hybridisation of neural networks and genetic algorithms for time-optimal control. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 389–396.

    Google Scholar 

  32. Jianer Chen, Iyad A. Kanj and Weijia Jia (1999) Vertex cover: further observations and further improvements. In: Proceedings of the 25th International Worksh. Graph-Theoretic Concepts in Computer Science, number 1665 in Lecture Notes in Computer Science. Springer-Verlag, pp. 313–324.

    Google Scholar 

  33. R. Cheng and M. Gen (1996) Parallel machine scheduling problems using memetic algorithms. In: 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems, Vol. 4. IEEE, New York, NY, pp. 2665–2670.

    Google Scholar 

  34. R. Cheng, M. Gen and Y. Tsujimura (1999) A tutorial survey of job-shop scheduling problems using genetic algorithms, ii. hybrid genetic search strategies. Computers & Industrial Engineering, 37(1–2), 51–55.

    Google Scholar 

  35. R.W. Cheng and M. Gen (1997) Parallel machine scheduling problems using memetic algorithms. Computers & Industrial Engineering, 33(3–4), 761–764.

    Google Scholar 

  36. P.C. Chu and J. Beasley (1997) A genetic algorithm for the generalised assignment problem. Computers & Operations Research, 24, 17–23.

    Article  MathSciNet  Google Scholar 

  37. D.E. Clark and D.R. Westhead (1996) Evolutionary algorithms in computer-aided molecular design. Journal of Computer-aided Molecular Design, 10(4), 337–358.

    Article  Google Scholar 

  38. H.G. Cobb and J.J. Grefenstette (1993) Genetic algorithms for tracking changing environments. In: S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 529–530.

    Google Scholar 

  39. P.E. Coll, G.A. Durán and P. Moscato (1999) On worst-case and comparative analysis as design principles for efficient recombination operators: A graph coloring case study. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 279–294.

    Google Scholar 

  40. D. Costa (1995) An evolutionary tabu search algorithm and the NHL scheduling problem. INFOR, 33(3), 161–178.

    MATH  Google Scholar 

  41. D. Costa, N. Dubuis and A. Hertz (1995) Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs. Journal of Heuristics, 1(1), 105–128.

    Google Scholar 

  42. C. Cotta (1998) A study of hybridisation techniques and their application to the design of evolutionary algorithms. AI Communications, 11(3–4), 223–224.

    Google Scholar 

  43. C. Cotta, E. Alba and J.M. Troya (1998) Utilising dynastically optimal form a recombination in hybrid genetic algorithms. In: A.E. Eiben, Th. Bäck, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving From Nature V, number 1498 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 305–314.

    Google Scholar 

  44. C. Cotta, E. Alba and J.M. Troya (1999) Stochastic reverse hillclimbing and iterated local search. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 1558–1565.

    Google Scholar 

  45. C. Cotta and J.M. Troya (1998) A hybrid genetic algorithm for the 0–1 multiple knapsack problem. In: G.D. Smith, N.C. Steele and R.F. Albrecht (eds.), Artificial Neural Nets and Genetic Algorithms 3. Springer-Verlag, Wien New York, pp.251–255.

    Google Scholar 

  46. C. Cotta and J.M. Troya (2000) On the influ ence of the representation granularity in heuristic forma recombination. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 433–439.

    Google Scholar 

  47. C. Cotta and J.M. Troya (2000) Using a hybrid evolutionary A* approach for learning reactive behaviors. In: S. Cagnoni et al. (eds.), Real-World Applications of Evolutionary Computation, volume 1803 of tLecture Notes in Computer Science. Springer-Verlag, Edinburgh, pp. 347–356.

    Google Scholar 

  48. T. Crain, R. Bishop, W. Fowler and K. Rock (1999) Optimal interplanetary trajectory design via hybrid genetic algorithm/recursive quadratic program search. In: Ninth AAS/AIAA Space Flight Mechanics Meeting. Breckenridge CO, pp. 99–133.

    Google Scholar 

  49. T. Dandekar and P. Argos (1996) Identifying the tertiary fold of small proteins with different topologies from sequence and secondary structure using the genetic algorithm and extended criteria specific for strand regions. Journal of Molecular Biology, 256(3), 645–660.

    Article  Google Scholar 

  50. Y. Davidor and O. Ben-Kiki (1992) The interplay among the genetic algorithm operators: Information theory tools used in a holistic way. In: R. Männer and B. Manderick (eds.), Parallel Problem Solving From Nature II. Elsevier Science Publishers B.V., Amsterdam, pp. 75–84.

    Google Scholar 

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

    Google Scholar 

  52. R. Dawkins (1976) The Selfish Gene. Clarendon Press, Oxford.

    Google Scholar 

  53. P. de Causmaecker, G. van den Berghe and E.K. Burke (1999) Using tabu search as a local heuristic in a memetic algorithm for the nurse rostering problem. In: Proceedings of the Thirteenth Conference on Quantitative Methods for Decision Making, pages abstract only, poster presentation. Brussels, Belgium.

    Google Scholar 

  54. P.A. de Souza, R. Garg and V.K. Garg (1998) Automation of the analysis of Mossbauer spectra. Hyperfine Interactions, 112(1–4), 275–278.

    Google Scholar 

  55. D.M. Deaven and K.O. Ho (1995) Molecular-geometry optimization with a genetic algorithm. Physical Review Letters, 75(2), 288–291.

    Article  Google Scholar 

  56. D.M. Deaven, N. Tit, J.R. Morris and K.M. Ho (1996) Structural optimization of Lennard-Jones clusters by a genetic algorithm. Chemical Physics Letters, 256(1–2), 195–200.

    Google Scholar 

  57. N. Dellaert and J. Jeunet (2000) Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm. International Journal of Production Research, 38(5), 1083–1099.

    Google Scholar 

  58. J.R. Desjarlais and T.M. Handel (1995) New strategies in protein design. Current Opinion in Biotechnology, 6(4), 460–466.

    Article  Google Scholar 

  59. R. Doll and M.A. VanHove (1996) Global optimization in LEED structure determination using genetic algorithms. Surface Science, 355(1–3), L393–L398.

    Google Scholar 

  60. R. Dorne and J.K. Hao (1998) A new genetic local search algorithm for graph coloring. In: A.E. Eiben, Th. Bäck, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving From Nature V, volume 1498 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 745–754.

    Google Scholar 

  61. R. Englemore and T. Morgan (eds.) (1988) Blackboard Systems. Addison-Wesley.

    Google Scholar 

  62. C. Ersoy and S.S. Panwar (1993) Topological design of interconnected LAN/MAN networks. IEEE Journal on Selected Areas in Communications, 11(8), 1172–1182.

    Article  Google Scholar 

  63. J. Fang and Y. Xi (1997) A rolling horizon job shop rescheduling strategy in the dynamic environment. International Journal of Advanced Manufacturing Technology, 13(3), 227–232.

    Google Scholar 

  64. C. Fleurent and J.A. Ferland (1997) Genetic and hybrid algorithms for graph coloring. Annals of Operations Research, 63, 437–461.

    Google Scholar 

  65. P.M. França, A.S. Mendes and P. Moscato (1999) Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times. In: Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece, pp. 1708–1710.

    Google Scholar 

  66. B. Freisleben and P. Merz (1996) A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, Nagoya, Japan. IEEE Press, pp. 616–621.

    Google Scholar 

  67. B. Freisleben and P. Merz (1996) New genetic local search operators for the traveling salesman problem. In: H.-M. Voigt, W. Ebeling, I. Rechenberg and H.-P. Schwefel (eds.), Parallel Problem Solving from Nature IV, volume 1141 of Lecture Notes in Computer Science. Springer, pp. 890–900.

    Google Scholar 

  68. R.T. Fu, K. Esfarjani, Y. Hashi, J. Wu, X. Sun and Y. Kawazoe (1997) Surface reconstruction of Si (001) by genetic algorithm and simulated annealing method. Science Reports of The Research Institutes Tohoku University Series A-Physics Chemistry and Metallurgy, 44(1), 77–81.

    Google Scholar 

  69. B.L. Garcia, P. Mahey and L.J. LeBlanc (1998) Iterative improvement methods for a multiperiod network design problem. European Journal of Operational Research, 110(1), 150–165.

    Article  Google Scholar 

  70. M. Gen and R. Cheng (1997) Genetic Algorithms and Engineering Design. Wiley Series in Engineering Design and Automation. John Wiley & Sons (Sd).

    Google Scholar 

  71. M. Gen, K. Ida and L. Yinzhen (1998) Bicriteria transportation problem by hybrid genetic algorithm. Computers & Industrial Engineering, 35(1–2), 363–366.

    Google Scholar 

  72. A.J. Goldstein and A.B. Lesk (1975) Common feature techniques for discrete optimization. Comp. Sci. Tech. Report 27, Bell. Tel. Labs, March.

    Google Scholar 

  73. M. Gorges-Schleuter (1989) ASPARAGOS: An asynchronous parallel genetic optimization strategy. In: J. David Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, pp. 422–427.

    Google Scholar 

  74. M. Gorges-Schleuter (1991) Explicit parallelism of genetic algorithms through population structures. In: H.-P. Schwefel and R. Manner (eds.), Parallel Problem Solving from Nature. Springer-Verlag, pp. 150–159.

    Google Scholar 

  75. M. Gorges-Schleuter (1991) Genetic Algorithms and Population Structures—A Massively Parallel Algorithm. PhD thesis, University of Dortmund, Germany.

    Google Scholar 

  76. M. Gorges-Schleuter (1997) Asparagos96 and the Traveling Salesman Problem. In: T. Baeck, Z. Michalewicz and X. Yao (eds.), Proceedings of the 1997 IEEE International Conference on Evolutionary Computation. Indianapolis, USA. IEEE Press, pp. 171–174.

    Google Scholar 

  77. J. Gottlieb (2000) Permutation-based evolutionary algorithms for multidimensional knapsack problems. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 408–114.

    Google Scholar 

  78. J. Gottlieb and T. Kruse (2000) Selection in evolutionary algorithms for the traveling salesman problem. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 415–421.

    Google Scholar 

  79. J. J. Grefenstette (1987) Incorporating Problem Specific Knowledge into Genetic Algorithms. In: L. Davis (ed.), Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Morgan Kaufmann Publishers, pp. 42–60.

    Google Scholar 

  80. P. Grim (1997) The undecidability of the spatialized prisoner’s dilemma. Theory and Decision, 42(1), 53–80.

    Article  MATH  MathSciNet  Google Scholar 

  81. J.B. Grimbleby (1999) Hybrid genetic algorithms for analogue network synthesis. In: Proceedings of the 1999 Congress on Evolutionary Computation, IEEE, Washington D.C., pp. 1781–1787.

    Google Scholar 

  82. J.R. Gunn (1997) Sampling protein conformations using segment libraries and a genetic algorithm. Journal of Chemical Physics, 106(10), 4270–4281.

    Article  Google Scholar 

  83. M. Guotian and L. Changhong (1999) Optimal design of the broadband stepped impedance transformer based on the hybrid genetic algorithm. Journal of Xidian University, 26(1), 8–12.

    Google Scholar 

  84. O.C.L. Haas, K.J. Burnham and J.A. Mills (1998) Optimization of beam orientation in radiotherapy using planar geometry. Physics in Medicine and Biology, 43(8), 2179–2193.

    Article  Google Scholar 

  85. O.C.L. Haas, K.J. Burnham, J.A. Mills, C.R. Reeves and M.H. Fisher (1996) Hybrid genetic algorithms applied to beam orientation in radiotherapy. In: Fourth European Congress on Intelligent Techniques and Soft Computing Proceedings, Vol. 3. Verlag Mainz, Aachen, Germany, pp. 2050–2055.

    Google Scholar 

  86. A.B. Hadj-Alouane, J.C. Bean and K.G. Murty (1999) A hybrid genetic/optimization algorithm for a task allocation problem. Journal of Scheduling, 2(4).

    Google Scholar 

  87. S.P. Harris and E.G. Ifeachor (1998) Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Transactions on Signal Processing, 46(12), 3304–3314.

    Article  Google Scholar 

  88. W.E. Hart and R.K. Belew (1991) Optimizing an arbitrary function is hard for the genetic algorithm. In: R.K. Belew and L.B. Booker (eds.), Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo CA, pp. 190–195.

    Google Scholar 

  89. B. Hartke (1993) Global geometry optimization of clusters using genetic algorithms. Journal of Physical Chemistry, 97(39), 9973–9976.

    Article  Google Scholar 

  90. M. Hifi (1997) A genetic algorithm-based heuristic for solving the weighted maximum independent set and some equivalent problems. Journal of the Operational Research Society, 48(6), 612–622.

    Article  MATH  Google Scholar 

  91. R. Hirsch and C.C. Mullergoymann (1995) Fitting of diffusion-coefficients in a 3-compartment sustained-release drug formulation using a genetic algorithm. International Journal of Pharmaceutics, 120(2), 229–234.

    Article  Google Scholar 

  92. K.M. Ho, A.A. Shvartsburg, B.C. Pan, Z. Y. Lu, C.Z. Wang, J.G. Wacker, J.L. Fye and M.F. Jarrold (1998) Structures of medium-sized silicon clusters. Nature, 392(6676), 582–585.

    Google Scholar 

  93. S. Hobday and R. Smith (1997) Optimisation of carbon cluster geometry using a genetic algorithm. Journal of The Chemical Society-Faraday Transactions, 93(22), 3919–3926.

    Google Scholar 

  94. R.J.W. Hodgson (2000) Memetic algorithms and the molecular geometry optimization problem. In: Proceedings of the 2000 Congress on Evolutionary Computation. IEEE Service Center, Piscataway, NJ, pp. 625–632.

    Google Scholar 

  95. Reimar Hofmann (1993) Examinations on the algebra of genetic algorithms. Master’s thesis, Technische Universität München, Institut fü Informatik.

    Google Scholar 

  96. D.R. Hofstadter (1983) Computer tournaments of the prisoners-dilemma suggest how cooperation evolves. Scientific American, 248(5), 16–23.

    Google Scholar 

  97. D. Holstein and P. Muscat (1999) Memetic algorithms using guided local search: A case study. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 235–244.

    Google Scholar 

  98. E. Hopper and B. Turton (1999) A genetic algorithm for a 2d industrial packing problem. Computers & Industrial Engineering, 37(1–2), 375–378.

    Google Scholar 

  99. M. Hulin (1997) An optimal stop criterion for genetic algorithms: A bayesian approach. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 135–143.

    Google Scholar 

  100. T. Ichimura and Y. Kuriyama (1998) Learning of neural networks with parallel hybrid ga using a royal road function. In: 1998 IEEE International Joint Conference on Neural Networks, Vol. 2, IEEE, New York, NY, pp. 1131–1136.

    Google Scholar 

  101. J. Berger, M. Salois and R. Begin (1998) A hybrid genetic algorithm for the vehicle routing problem with time windows. In: R.E. Mercer and E. Neufeld (eds.), Advances in Artificial Intelligence. 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence. Springer-Verlag, Berlin, pp. 114–127.

    Google Scholar 

  102. W.R. Jih and Y.J. Hsu (1999) Dynamic vehicle routing using hybrid genetic algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 453–458.

    Google Scholar 

  103. D.S. Johnson and L.A. McGeoch (1997) The traveling salesman problem: Acase study. In: E.H.L. Aarts and J.K. Lenstra (eds,), Local Search in Combinatorial Optimization. Wiley, Chichester, pp. 215–310.

    Google Scholar 

  104. D.S. Johnson, C.H. Papadimitriou and M. Yannakakis (1988) How easy is local search? Journal of Computers and System Sciences, 37, 79–100.

    MathSciNet  Google Scholar 

  105. G. Jones, P. Willett, R.C. Glen, A.R. Leach and R. Taylor (1997) Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3), 727–748.

    Article  Google Scholar 

  106. T.C. Jones (1995) Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University ofNew Mexico.

    Google Scholar 

  107. B.M. Kariuki, H. Serrano-Gonzalez, R.L. Johnston and K.D.M. Harris (1997) The application of a genetic algorithm for solving crystal structures from powder diffraction data. Chemical Physics Letters, 280(3–4), 189–195.

    Google Scholar 

  108. R.M. Karp (1972) Reducibility among combinatorial problems. In: R.E. Miller and J.W. Thatcher (eds.), Complexity of Computer Computations. Plenum, New York NY, pp. 85–103.

    Google Scholar 

  109. I.E. Kassotakis, M.E. Markaki and A.V. Vasilakos (2000) A hybrid genetic approach for channel reuse in multiple access telecommunication networks. IEEE Journal on Selected Areas in Communications, 18(2), 234–243.

    Article  Google Scholar 

  110. K. Katayama, H. Hirabayashi and H. Narihisa (1998) Performance analysis for crossover operators of genetic algorithm. Transactions of the Institute of Electronics, Information and Communication Engineers, J81D-I(6), 639–650.

    Google Scholar 

  111. T.S. Kim and G.S. May (1999) Intelligent control of via formation by photosensitive BCB for MCM-L/D applications. IEEE Transactions on Semiconductor Manufacturing, 12, 503–515.

    Google Scholar 

  112. S. Kirkpatrick and G. Toulouse (1985) Configuration space analysis of traveling salesman problems. J. Physique, 46, 1277–1292.

    MathSciNet  Google Scholar 

  113. K. Krishna and M. Narasimha-Murty (1999) Genetic k-means algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 29(3), 433–439.

    Article  Google Scholar 

  114. K. Krishna, K.R. Ramakrishnan and M.A.L. Thathachar (1997) Vector quantization using genetic k-means algorithm for image compression. In: 1997 International Conference on Information, Communications and Signal Processing, Vol. 3, IEEE, New York, NY, pp. 1585–1587.

    Google Scholar 

  115. R.M. Krzanowski and J. Raper (1999) Hybrid genetic algorithm for transmitter location in wireless networks. Computers, Environment and Urban Systems, 23(5), 359–382.

    Article  Google Scholar 

  116. E. Lamma, L.M. Pereira and F. Riguzzi (2000) Multi-agent logic aided lamarckian learning. Technical Report DEIS-LIA-00-004, Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna (Italy), LIA Series no. 44 (submitted for publication).

    Google Scholar 

  117. E. Landree, C. Collazo-Davila and L.D. Marks (1997) Multi-solution genetic algorithm approach to surface structure determination using direct methods. Acta Crystallographica Section B—Structural Science, 53, 916–922.

    Google Scholar 

  118. G.A. Lazar, J.R. Desjarlais and T.M. Handel (1997) De novo design of the hydrophobic core ofubiquitin. Protein Science, 6(6), 1167–1178.

    Google Scholar 

  119. C.Y. Lee (1994) Genetic algorithms for single machine job scheduling with common due date and symmetric penalties. Journal of the Operations Research Society of Japan, 37(2), 83–95.

    MATH  MathSciNet  Google Scholar 

  120. D. Levine (1996) A parallel genetic algorithm for the set partitioning problem. In: I.H. Osman and J.P. Kelly (eds.), Meta-Heuristics: Theory & Applications. Kluwer Academic Publishers, pp. 23–35.

    Google Scholar 

  121. H.R. Lewis and C.H. Papadimitriou (1998) Elements of the Theory of Computation. Prentice-Hall, Inc., Upper Saddle River, New Jersey.

    Google Scholar 

  122. F. Li, R. Morgan and D. Williams (1996) Economic environmental dispatch made easy with hybrid genetic algorithms. In: Proceedings of the International Conference on Electrical Engineering, Vol. 2. Int. Acad. Publishers, Beijing, China, pp. 965–969.

    Google Scholar 

  123. L.P. Li, T.A. Darden, S.J. Freedman, B.C. Furie, B. Furie, J.D. Baleja, H. Smith, R.G. Hiskey and L.G. Pedersen (1997) Refinement of the NMR solution structure of the gamma-carboxyglutamic acid domain of coagulation factor IX using molecular dynamics simulation with initial Ca2+ positions determined by a genetic algorithm. Biochemistry, 36(8), 2132–2138.

    Article  Google Scholar 

  124. C.F. Liaw (2000) A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Oprational Research, 124(1), 28–42.

    MATH  MathSciNet  Google Scholar 

  125. G.E. Liepins and M.R. Hilliard (1987) Greedy Genetics. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, Cambridge, MA, pp. 90–99.

    Google Scholar 

  126. S. Lin (1965) Computer solutions of the traveling salesman problem. Bell System Technical Journal, 10, 2245–2269.

    Google Scholar 

  127. S. Lin and B. Kernighan (1973) An Effective Heuristic Algorithm for the Traveling Salesman Problem. Operations Research, 21, 498–516.

    MathSciNet  Google Scholar 

  128. S.E. Ling (1992) Integrating genetic algorithms with a prolog assignment program as a hybrid solution for a polytechnic timetable problem. In: Parallel Problem Solvingfrom Nature II. Elsevier Science Publisher B.V., pp. 321–329.

    Google Scholar 

  129. D.M. Lorber and B.K. Shoichet (1998) Flexible ligand docking using conformational ensembles. Protein Science, 7(4), 938–950.

    Google Scholar 

  130. S.J, Louis, X. Yin and Z.Y. Yuan (1999) Multiple vehicle routing with time windows using genetic algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation. Washington D.C., pp. 1804–1808. IEEE Neural Network Council—Evolutionary Programming Society—Institution of Electrical Engineers.

    Google Scholar 

  131. A.L. MacKay (1995) Generalized crystallography. THEOCHEM-Journal of Molecular Structure, 336(2–3), 293–303.

    Google Scholar 

  132. J. Maddox (1995) Genetics helping molecular-dynamics. Nature, 376(6537), 209–209.

    Google Scholar 

  133. K.E. Mathias and L.D. Whitley (1994) Noisy function evaluation and the delta coding algorithm. In: Proceedings of the SPIE—The International Society for Optical Engineering, pp. 53–64.

    Google Scholar 

  134. A.C.W. May and M.S. Johnson (1994) Protein-structure comparisons using a combination of a genetic algorithm, dynamic-programming and least-squares minimization. Protein Engineering, 7(4), 475–485.

    Google Scholar 

  135. A.S. Mendes, F.M. Muller, P.M. França and P. Moscato (1999) Comparing meta-heuristic approaches for parallel machine scheduling problems with sequence-dependent setup times. In: Proceedings of the 15th International Conference on CAD/CAM Robotics & Factories of the Future, Aguas de Lindoia, Brazil.

    Google Scholar 

  136. L.D. Merkle, G.B. Lament, G.H. Jr. Gates and R. Pachter (1996) Hybrid genetic algorithms for minimization of a polypeptide specific energy model. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation. IEEE, New York, NY, pp. 396–400.

    Google Scholar 

  137. P. Merz and B. Freisleben (1997) A Genetic Local Search Approach to the Quadratic Assignment Problem. In: T. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 465–172.

    Google Scholar 

  138. P. Merz and B. Freisleben (1997) Genetic local search for the TSP: new results. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation. IEEE Press, pp. 159–164.

    Google Scholar 

  139. P. Merz and B. Freisleben (1998) Memetic algorithms and the fitness landscape of the graph bi-partitioning problem. In: A.E. Eiben, T. Back, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving from Nature V, volume 1498 of Lecture Notes in Computer Science. Springer-Verlag, pp. 765–774.

    Google Scholar 

  140. P. Merz and B. Freisleben (1998) On the effectiveness of evolutionary search in high-dimensional NK-landscapes. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE Press, pp. 741–745.

    Google Scholar 

  141. P. Merz and B. Freisleben (1999) A comparion of Memetic Algorithms, Tabu Search, and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 Congress on Evolutionary Computation. Washington D.C. IEEE Service Center, Piscataway, NJ, pp. 2063–2070.

    Google Scholar 

  142. P. Merz and B. Freisleben (1999) Fitness landscapes and memetic algorithm design. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 245–260.

    Google Scholar 

  143. P. Merz and B. Freisleben (2000) Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning. Evolutionary Computation, 8(1), 61–91.

    Article  Google Scholar 

  144. J.C. Meza, R.S. Judson, T.R. Faulkner and A.M. Treasurywala (1996) A comparison of a direct search method and a genetic algorithm for conformational searching. Journal of Computational Chemistry, 17(9), 1142–1151.

    Article  Google Scholar 

  145. M. Mignotte, C. Collet, P. Pérez and P. Bouthemy (2000) Hybrid genetic optimization and statistical model based approach for the classification of shadow shapes in sonar imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(2), 129–141.

    Article  Google Scholar 

  146. D.M. Miller, H.C. Chen, J. Matson and Q. Liu (1999) A hybrid genetic algorithm for the single machine scheduling problem. Journal of Heuristics, 5(4), 437–454.

    Article  Google Scholar 

  147. S.T. Miller, J.M. Hogle and D.J. Filman (1996) A genetic algorithm for the ab initio phasing of icosahedral viruses. Acta Crystallographica Section D—Biological Crystallography, 52, 235–251.

    Google Scholar 

  148. L. Min and W. Cheng (1998) Identical parallel machine scheduling problem for minimizing the makespan using genetic algorithm combined with simulated annealing. Chinese Journal of Electronics, 7(4), 317–321.

    Google Scholar 

  149. X.G. Ming and K.L. Mak (2000) A hybrid hopfield network-genetic algorithm approach to optimal process plan selection. International Journal of Production Research, 38(8), 1823–1839.

    Google Scholar 

  150. M. Minsky (1994) Negative expertise. International Journal of Expert Systems, 7(1), 13–19.

    MathSciNet  Google Scholar 

  151. A. Monfroglio (1996) Hybrid genetic algorithms for timetabling. International Journal of Intelligent Systems, 11(8), 477–523.

    Article  MATH  Google Scholar 

  152. A. Monfroglio (1996) Timetabling through constrained heuristic search and genetic algorithms. Software—Practice and Experience, 26(3), 251–279.

    Google Scholar 

  153. A. Montfroglio (1996) Hybrid genetic algorithms for a rostering problem. Software—Practice and Experience, 26(7), 851–862.

    Google Scholar 

  154. P. Moscato (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report Caltech Concurrent Computation Program, Report 826, California Institute of Technology, Pasadena, California, USA.

    Google Scholar 

  155. P. Moscato (1993) An introduction to population approaches for optimization and hierarchical objective functions: the role of tabu search. Annals of Operations Research, 41(1–4), 85–121.

    MATH  Google Scholar 

  156. P. Moscato and M.G. Norman (1992) A Memetic Approach for the Traveling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems. In: M. Valero, E. Onate, M. Jane, J.L. Larriba and B. Suarez (eds.), Parallel Computing and Transputer Applications. IOS Press, Amsterdam, pp. 177–186.

    Google Scholar 

  157. H. Mühlenbein (1991) Evolution in time and space—the parallel genetic algorithm. In: Gregory J.E. Rawlins (ed.), Foundations of Genetic Algorithms. Morgan Kaufmann Publishers, pp. 316–337.

    Google Scholar 

  158. H. Mühlenbein M. Gorges-Schleuter and O. Krämer (1988) Evolution Algorithms in Combinatorial Optimization. Parallel Computing, 7, 65–88.

    Article  Google Scholar 

  159. T. Murata and H. Ishibuchi (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 2. IEEE, New York, NY, pp. 812–817.

    Google Scholar 

  160. T. Murata, H. Ishibuchi and H. Tanaka (1996) Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061–1071.

    Google Scholar 

  161. M. Musil, M.J. Wilmut and N.R. Chapman (1999) A hybrid simplex genetic algorithm for estimating geoacoustic parameters using matched-field inversion. IEEE Journal of Oceanic Engineering, 24(3), 358–369.

    Article  Google Scholar 

  162. Y. Nagata and Sh. Kobayashi (1997) Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, EUA. Morgan Kaufmann, San Mateo, CA, pp. 450–457.

    Google Scholar 

  163. M. Nakamaru, H. Matsuda and Y. Iwasa (1998) The evolution of social interaction in lattice models. Sociological Theory and Methods, 12(2), 149–162.

    Google Scholar 

  164. M. Nakamaru, H. Nogami and Y. Iwasa (1998) Score-dependent fertility model for the evolution of cooperation in a lattice. Journal of Theoretical Biology, 194(1), 101–124.

    Article  Google Scholar 

  165. R. Niedermeier and P. Rossmanith (2000) An efficient fixed parameter algorithm for 3-hitting set. Technical Report WSI-99-18, Universität Tübingen, Wilhelm-Schickard-Institut für Informatik, 1999. Technical Report, Revised version accepted in Journal of Discrete Algorithms, August.

    Google Scholar 

  166. R. Niedermeier and P. Rossmanith (2000) A general method to speed up fixed-parameter-tractable algorithms. Information Processing Letters, 73, 125–129.

    Article  MathSciNet  Google Scholar 

  167. J.A. Niesse and H.R. Mayne (1996) Global geometry optimization of atomic clusters using a modified genetic algorithm in space-fixed coordinates. Journal of Chemical Physics, 105(11), 4700–1706.

    Article  Google Scholar 

  168. A.L. Nordstrom and S. Tufekci (1994) A genetic algorithm for the talent scheduling problem. Computers & Operations-Research, 21(8), 927–940.

    Google Scholar 

  169. M.G. Norman and P. Moscato (1991) A competitive and cooperative approach to complex combinatorial search. Technical Report Caltech Concurrent Computation Program, Report. 790, California Institute of Technology, Pasadena, California, USA, 1989. expanded version published at the Proceedings of the 20th Informatics and Operations Research Meeting, Buenos Aires (20th JAIIO), August pp. 3.15–3.29.

    Google Scholar 

  170. A.G.N. Novaes, J.E.S. De-Cursi and O.D. Graciolli (2000) A continuous approach to the design of physical distribution systems. Computers & Operations Research, 27(9), 877–893.

    Article  Google Scholar 

  171. M.A. Nowak and K. Sigmund (1998) Evolution of indirect reciprocity by image scoring. Nature, 393(6685), 573–577.

    Article  Google Scholar 

  172. P. Osmera (1995) Hybrid and distributed genetic algorithms for motion control. In: V. Chundy and E. Kurekova (eds.), Proceedings of the Fourth International Symposium on Measurement and Control in Robotics, pp. 297–300.

    Google Scholar 

  173. R. Ostermark (1999) A neuro-genetic algorithm for heteroskedastic time-series processes: empirical tests on global asset returns. Soft Computing, 3(4), 206–220.

    Google Scholar 

  174. R. Ostermark (1999) Solving a nonlinear non-convex trim loss problem with a genetic hybrid algorithm. Computers & Operations Research, 26(6), 623–635.

    Google Scholar 

  175. R. Ostermark (1999) Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm. Computational Economics, 13(2), 103–115.

    Google Scholar 

  176. E. Ozcan and C.K. Mohan (1998) Steady state memetic algorithm for partial shape matching. In: V.W. Porto, N. Saravanan and D. Waagen (eds.), Evolutionary Programming VII, volume 1447 of Lecture Notes in Computer Science. Springer, Berlin, pp. 527–236.

    Google Scholar 

  177. L. Ozdamar (1999) A genetic algorithm approach to a general category project scheduling problem. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 29(1), 44–59.

    Article  Google Scholar 

  178. M.N. Pacey, X.Z. Wang, S.J. Haake and E.A. Patterson (1999) The application of evolutionary and maximum entropy algorithms to photoelastic spectral analysis. Experimental Mechanics, 39(4), 265–273.

    Google Scholar 

  179. B. Paechter, A. Cumming, M.G. Norman and H. Luchian Extensions to a Memetic timetabling system. In: E.K. Burke and P. Ross (eds.), The Practice and Theory of Automated Timetabling, volume 1153 of Lecture Notes in Computer Science. Springer Verlag, pp. 251–265.

    Google Scholar 

  180. B. Paechter, R.C. Rankin and A. Cumming (1998) Improving a lecture timetabling system for university wide use. In: E.K. Burke and M. Carter (eds.), The Practice and Theory of Automated Timetabling II, volume 1408 of Lecture Notes in Computer Science. Springer Verlag, pp. 156–165.

    Google Scholar 

  181. C.H. Papadimitriou and K. Steiglitz (1982) Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.

    Google Scholar 

  182. M. Peinado and T. Lengauer (1997) Parallel “go with the winners algorithms” in the LogP Model. In: IEEE Computer Society Press (ed.), Proceedings of the 11th International Parallel Processing Symposium. Los Alamitos, California, pp. 656–664.

    Google Scholar 

  183. O.K. Pratihar, K. Deb and A. Ghosh (1999) Fuzzy-genetic algorithms andmobile robot navigation among static obstacles. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 327–334.

    Google Scholar 

  184. N. Pucello, M. Rosati, G. D’Agostino, F. Pisacane, V. Rosato and M. Celino (1997) Search of molecular ground state via genetic algorithm: Implementation on a hybrid SIMD-MIMD platform. International Journal of Modern Physics C. 8(2), 239–252.

    Google Scholar 

  185. W.J. Pullan (1997) Structure prediction of benzene clusters using a genetic algorithm. Journal of Chemical Information and Computer Sciences, 37(6), 1189–1193.

    Article  Google Scholar 

  186. D. Quagliarella and A. Vicini (1998) Hybrid genetic algorithms as tools for complex optimisation problems. In: P. Blonda, M. Castellano and A. Petrosino (eds.), New Trends in Fuzzy Logic II. Proceedings of the Second Italian Workshop on Fuzzy Logic. World Scientific, Singapore, pp. 300–307.

    Google Scholar 

  187. N.J. Radcliffe (1994) The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence, 10, 339–384.

    Article  MATH  MathSciNet  Google Scholar 

  188. N.J. Radcliffe and P.D. Surry (1994) Fitness Variance of Formae and Performance Prediction. In: L.D. Whitley and M.D. Vose (eds.), Proceedings of the Third Workshop on Foundations of Genetic Algorithms. Morgan Kaufmann, San Francisco, pp. 51–72.

    Google Scholar 

  189. N.J. Radcliffe and P.D. Surry (1994) Formal Memetic Algorithms. In: T. Fogarty (ed.), Evolutionary Computing: AISB Workshop, volume 865 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 1–16.

    Google Scholar 

  190. G.R. Raidl and B.A. Julstron (2000) A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 440–45.

    Google Scholar 

  191. E. Ramat, G. Venturini, C. Lente and M. Slimane (1997) Solving the multiple resource constrained project scheduling problem with a hybrid genetic algorithm. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco CA, pp. 489–496.

    Google Scholar 

  192. R.C. Rankin (1996) Automatic timetabling in practice. In: Practice and Theory of Automated Timetabling. First International Conference. Selected Papers. Springer-Verlag, Berlin, pp. 266–279.

    Google Scholar 

  193. M.L. Raymer, P.C. Sanschagrin, W.F. Punch, S. Venkataraman, E.D. Goodman and L.A. Kuhn (1997) Predicting conserved water-mediated and polar ligand interactions in proteins using a k-nearest-neighbors genetic algorithm. Journal of Molecular Biology, 265(4), 445–464.

    Article  Google Scholar 

  194. I. Rechenberg (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart.

    Google Scholar 

  195. C. Reeves (1996) Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63, 371–396.

    Article  MATH  Google Scholar 

  196. C. Reich (2000) Simulation if imprecise ordinary differential equations using evolutionary algorithms. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 428–432.

    Google Scholar 

  197. M. A. Ridao, J. Riquelme, E.F. Camacho and M. Toro (1998) An evolutionary and local search algorithm for planning two manipulators motion. In: A.P. Del Pobil, J. Mira and M. Ali (eds.), Tasks and Methods in Applied Artificial Intelligence, volume 1416 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, pp. 105–114.

    Google Scholar 

  198. C.F. Ruff, S.W. Hughes and D.J. Hawkes (1999) Volume estimation from sparse planar images using deformable models. Image and Vision Computing, 17(8), 559–565.

    Article  Google Scholar 

  199. S.M. Sait and H. Youssef (2000) VLSI Design Automation: Theory and Practice. McGraw-Hill Book Co. (copublished by IEEE), Europe.

    Google Scholar 

  200. A. Sakamoto, X.Z. Liu and T. Shimamoto (1997) A genetic approach for maximum independent set problems. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E80A(3), 551–556.

    Google Scholar 

  201. V. Schnecke and O. Vornberger (1997) Hybrid genetic algorithms for constrained placement problems. IEEE Transactions on Evolutionary Computation, 1(4), 266–277.

    Article  Google Scholar 

  202. H.-P. Schwefel (1984) Evolution strategies: a family ofnon-linear optimization techniques based on imitating some principles of natural evolution. Annals of Operations Research, 1, 165–167.

    Article  Google Scholar 

  203. K. Shankland, W.I.F. David and T. Csoka (1997) Crystal structure determination from powder diffraction data by the application of a genetic algorithm. Zeitschrift Fur Kristallographie, 212(8), 550–552.

    Google Scholar 

  204. K. Shankland, W.I.F. David T. Csoka and L. McBride (1998) Structure solution of ibuprofen from powder diffraction data by the application of a genetic algorithm combined with prior conformational analysis. International Journal of Pharmaceutics, 165(1), 117–126.

    Google Scholar 

  205. D. Srinivasan, R.L. Cheu, Y.P. Poh and A.K.C. Ng (2000) Development of an intelligent technique for traffic network incident detection. Engineering Applications of Artificial Intelligence, 13(3), 311–322.

    Article  Google Scholar 

  206. K. Steiglitz and P. Weiner (1968) Some improved algorithms for computer solution of the traveling salesman problem. In: Proceedings of the Sixth Allerton Conference on Circuit and System Theory. Urbana, Illinois, pp. 814–821.

    Google Scholar 

  207. J.Y. Suh and Dirk Van Gucht (1987) Incorporating heuristic information into genetic search. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates. Cambridge, MA, pp. 100–107.

    Google Scholar 

  208. P.D. Surry and N. J. Radcliffe (1996) Inoculation to initialise evolutionary search. In: T.C. Fogarty (ed.), Evolutionary Computing: AISB Workshop, number 1143 in Lecture Notes in Computer Science. Springer-Verlag, pp. 269–285.

    Google Scholar 

  209. G. Syswerda (1989) Uniform crossover in genetic algorithms. In: J.D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 2–9.

    Google Scholar 

  210. T. Taguchi, T. Yokota and M. Gen (1998) Reliability optimal design problem with interval coefficients using hybrid genetic algorithms. Computers & Industrial Engineering, 35(1–2), 373–376.

    Google Scholar 

  211. K.Y. Tam and R.G. Compton (1995) GAMATCH—a genetic algorithm-based program for indexing crystal faces. Journal of Applied Crystallography, 28, 640–645.

    Article  Google Scholar 

  212. A.P. Topchy, O.A. Lebedko and V.V. Miagkikh (1996) Fast learning in multi-layered networks by means of hybrid evolutionary and gradient algorithms. In: Proceedings of International Conference on Evolutionary Computation and its Applications, pp. 390–398.

    Google Scholar 

  213. A.J. Urdaneta, J.F. Gómez, E. Sorrentino, L. Flores and R. Díaz (1999) A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming. IEEE Transactions on Power Systems, 14(4), 1292–1298.

    Article  Google Scholar 

  214. A.H.C. vanKampen, C.S. Strom and L.M.C Buydens (1996) Legalization, penalty and repair functions for constraint handling in the genetic algorithm methodology. Chemometrics and Intelligent Laboratory Systems, 34(1), 55–68.

    Google Scholar 

  215. L. Wang and J. Yen (1999) Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and kalman filter. Fuzzy Sets and Systems, 101(3), 353–362.

    Article  MathSciNet  Google Scholar 

  216. J.P. Watson, S. Rana, L.D. Whitley and A.E. Howe (1999) The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling. Journal of Scheduling, 2(2), 79–98.

    Article  MathSciNet  Google Scholar 

  217. R. Wehrens, C. Lucasius, L. Buydens and G. Kateman (1993) HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms. Analytica Chimica ACTA, 277(2), 313–324.

    Article  Google Scholar 

  218. P. Wei and L.X. Cheng (1999) A hybrid genetic algorithm for function optimization. Journal of Software, 10(8), 819–823.

    Google Scholar 

  219. X. Wei and F. Kangling (2000) A hybrid genetic algorithm for global solution of nondifferentiable nonlinear function. Control Theory & Applications, 17(2), 180–183.

    Google Scholar 

  220. D.S. Weile and E. Michielssen (1999) Design of doubly periodic filter and polarizer structures using a hybridized genetic algorithm. Radio Science, 34(1), 51–63.

    Article  Google Scholar 

  221. R.P. White, J.A. Niesse and H.R. Mayne (1998) A study of genetic algorithm approaches to global geometry optimization of aromatic hydrocarbon microclusters. Journal of Chemical Physics, 108(5), 2208–2218.

    Article  Google Scholar 

  222. D. Whitley (1987) Using reproductive evaluation to improve genetic search and heuristic discovery. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, Cambridge, MA, pp. 108–115.

    Google Scholar 

  223. P. Willett (1995) Genetic algorithms in molecular recognition and design. Trends in Biotechnology, 13(12), 516–521.

    Article  Google Scholar 

  224. D.H. Wolpert and W.G. Macready (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.

    Article  Google Scholar 

  225. J. Xiao and L. Zhang (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Transactions on Evolutionary Computation, 1(1), 18–28.

    Google Scholar 

  226. M. Yannakakis (1997) Computational complexity. In: E.H.L. Aarts and J.K. Lenstra (eds.), Local Search in Combinatorial Optimization. Wiley, Chichester, pp. 19–55.

    Google Scholar 

  227. X. Yao (1993) Evolutionary artificial neural networks. International Journal of Neural Systems, 4(3), 203–222.

    Article  Google Scholar 

  228. I.C. Yeh (1999) Hybrid genetic algorithms for optimization of truss structures. Computer Aided Civil and Infrastructure Engineering, 14(3), 199–206.

    Google Scholar 

  229. M. Yoneyama, H. Komori and S. Nakamura (1999) Estimation of impulse response of vocal tract using hybrid genetic algorithm—a case of only glottal source. Journal of the Acoustical Society of Japan, 55(12), 821–830.

    Google Scholar 

  230. C.R. Zacharias, M.R. Lemes and A.D. Pino (1998) Combining genetic algorithm and simulated annealing: a molecular geometry optimization study. THEOCHEM—Journal of Molecular Structure, 430(29–39).

    Google Scholar 

  231. M. Zwick, B. Lovell and J. Marsh (1996) Global optimization studies on the 1-d phase problem. International Journal of General Systems, 25(1), 47–59.

    Google Scholar 

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Moscato, P., Cotta, C. (2003). A Gentle Introduction to Memetic Algorithms. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 57. Springer, Boston, MA. https://doi.org/10.1007/0-306-48056-5_5

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