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Genetic Algorithms

  • Kumara Sastry
  • David Goldberg
  • Graham Kendall

Abstract

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957; Bremermann, 1958; Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology.

Keywords

Genetic Algorithm Evolutionary Computation Memetic Algorithm Simple Genetic Algorithm Uniform Crossover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Asoh, H. and Mühlenbein, H., 1994, On the mean convergence time of evolutionary algorithms without selection and mutation, Parallel Problem Solving from Nature III, Lecture Notes in Computer Science, Vol. 866, pp. 98–107.Google Scholar
  2. Bäck, T., 1995, Generalized convergence models for tournament—and (μ, λ)—selection, Proc. 6th Int. Conf. on Genetic Algorithms, pp. 2–8.Google Scholar
  3. Bäck, T., Fogel, D. B. and Michalewicz, Z., 1997, Handbook of Evolutionary Computation, Oxford University Press, Oxford.MATHGoogle Scholar
  4. Baker, J. E., 1985, Adaptive selection methods for genetic algorithms, Proc. Int. Conf. on Genetic Algorithms and Their Applications, pp. 101–111.Google Scholar
  5. Baluja, S., 1994, Population-based incremental learning: A method of integrating genetic search based function optimization and competitive learning, Technical Report CMU-CS-94-163, Carnegie Mellon University.Google Scholar
  6. Barthelemy, J.-F. M. and Haftka, R. T., 1993, Approximation concepts for optimum structural design—a review, Struct. Optim. 5:129–144.CrossRefGoogle Scholar
  7. Beasley, D., Bull, D. R. and Martin, R. R., 1993, An overview of genetic algorithms: Part 1, fundamentals, Univ. Comput. 15:58–69.Google Scholar
  8. Blickle, T. and Thiele, L., 1995, A mathematical analysis of tournament selection, Proc. 6th Int. Conf. on Genetic Algorithms, pp. 9–16.Google Scholar
  9. Booker, L. B., Fogel, D. B., Whitley, D. and Angeline, P. J., 1997, Recombination, in: The Handbook of Evolutionary Computation, T. Bäck, D. B. Fogel, and Z. Michalewicz, eds, chapter E3.3, pp. C3.3:1–C3.3:27, IOP Publishing and Oxford University Press, Philadelphia, PA.Google Scholar
  10. Bosman, P. A. N. and Thierens, D., 1999, Linkage information processing in distribution estimation algorithms, Proc. 1999 Genetic and Evolutionary Computation Conf., pp. 60–67.Google Scholar
  11. Bremermann, H. J., 1958, The evolution of intelligence. The nervous system as a model of its environment, Technical Report No. 1, Department of Mathematics, University of Washington, Seattle, WA.Google Scholar
  12. Bulmer, M. G., 1985, The Mathematical Theory of Quantitative Genetics, Oxford University Press, Oxford.Google Scholar
  13. Burke, E. K. and Newall, J. P., 1999, A multi-stage evolutionary algorithm for the timetable problem, IEEE Trans. Evol Comput. 3:63–74.CrossRefGoogle Scholar
  14. Burke, E. K. and Smith, A. J., 1999, A memetic algorithm to schedule planned maintenance, ACM J. Exp. Algor. 41, www.jea.acm.org/1999/BurkeMemetic/ ISSN 1084-6654.Google Scholar
  15. Burke, E. K. and Smith, A. J., 2000, Hybrid Evolutionary Techniques for the Maintenance Scheduling Problem, IEEE Trans. Power Syst. 15:122–128.CrossRefGoogle Scholar
  16. Burke, E. K., Elliman, D. G. and Weare, R.F., 1995, Specialised recombinative operators for timetabling problems, in: Evolutionary Computing: AISB Workshop 1995 T. Fogarty, ed., Lecture Notes in Computer Science, Vol. 993, pp. 75–85, Springer, Berlin.Google Scholar
  17. Burke, E. K., Newall, J. P. and Weare, R. F., 1996, A memetic algorithm for university exam timetabling, in: The Practice and Theory of Automated Timetabling I, E. K. Burke and P. Ross, eds, Lecture Notes in Computer Science, Vol. 1153, pp. 241–250, Springer, Berlin.Google Scholar
  18. Burke, E. K., Newall, J. P. and Weare, R. F., 1998, Initialisation strategies and diversity in evolutionary timetabling, Evol. Comput. J. (special issue on Scheduling) 6:81–103.Google Scholar
  19. Burke, E. K., Cowling, P. I., De Causmaecker, P. and Vanden Berghe, G., 2001, A memetic approach to the nurse rostering problem, Appl. Intell. 15:199–214.MATHCrossRefGoogle Scholar
  20. Cantü-Paz, E., 1997, A summary of research on parallel genetic algorithms IlliGAL Report No. 97003, General Engineering Department, University of Illinois at Urbana-Champaign, Urbana, IL.Google Scholar
  21. Cantú-Paz, E., 1999, Migration policies and takeover times in parallel genetic algorithms, in: Proc. Genetic and Evolutionary Computation Conf., p. 775, Morgan Kaufmann, San Francisco.Google Scholar
  22. Cantú-Paz, E., 2000, Efficient and Accurate Parallel Genetic Algorithms, Kluwer, Boston, MA.MATHGoogle Scholar
  23. Cheng, R. W. and Gen, M., 1997, Parallel machine scheduling problems using memetic algorithms, Comput. Indust. Eng., 33:761–764.CrossRefGoogle Scholar
  24. Coley, D. A., 1999, An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific, New York.Google Scholar
  25. Costa, D., 1995, An evolutionary tabu search algorithm and the nhl scheduling problem, INFOR 33:161–178.MATHGoogle Scholar
  26. Crow, J. F. and Kimura, M., 1970, An Introduction of Population Genetics Theory, Harper and Row, New York.Google Scholar
  27. Davis, L., 1985, Applying algorithms to epistatic domains, in: Proc. Int. Joint Conf. on Artifical Intelligence, pp. 162–164.Google Scholar
  28. Davis, L. D. (ed), 1987, Genetic Algorithms and Simulated Annealing, Pitman, London.MATHGoogle Scholar
  29. Davis, L. (ed), 1991, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York.Google Scholar
  30. De Jong, K. A., 1975, An analysis of the behavior of a class of genetic adaptive systems, Doctoral Dissertation, University of Michigan, Ann Arbor, MI (University Microfilms No. 76-9381) (Dissertation Abs. Int. 36:5140B).Google Scholar
  31. Deb, K. and Goldberg, D. E., 1994, Sufficient conditions for deceptive and easy binary functions, Ann. Math. Artif. Intell. 10:385–408.MATHCrossRefMathSciNetGoogle Scholar
  32. Falkenauer E., 1998, Genetic Algorithms and Grouping Problems, Wiley, New York.Google Scholar
  33. Fitzpatrick, J. M., Grefenstette, J. J. and Van Gucht, D., 1984, Image registration by genetic search, in: Proc. IEEE Southeast Conf., IEEE, Piscataway, NJ, pp. 460–464.Google Scholar
  34. Fleurent, C. and Ferland, J., 1994, Genetic hybrids for the quadratic assignment problem, in: DIMACS Series in Mathematics and Theoretical Computer Science, Vol. 16, pp. 190–206.MathSciNetGoogle Scholar
  35. Fogel, D. B., 1998, Evolutionary Computation: The Fossil Record, IEEE, Piscataway, NJ.MATHGoogle Scholar
  36. Forrest, S., 1993, Genetic algorithms: Principles of natural selection applied to computation, Science 261:872–878.CrossRefGoogle Scholar
  37. Fraser, A. S., 1957, Simulation of genetic systems by automatic digital computers. II: Effects of linkage on rates under selection, Austral. J. Biol. Sci. 10:492–499.Google Scholar
  38. Goldberg, D. E., 1983, Computer-aided pipeline operation using genetic algorithms and rule learning, Doctoral Dissertation,. University of Michigan, Ann Arbor, MI.Google Scholar
  39. Goldberg, D. E., 1987, Simple genetic algorithms and the minimal deceptive problem, in: Genetic Algorithms and Simulated Annealing, L. Davis, ed., chapter 6, pp. 74–88, Morgan Kaufmann, Los Altos, CA.Google Scholar
  40. Goldberg, D. E., 1989a, Genetic algorithms and Walsh functions: Part II, deception and its analysis, Complex Syst. 3:153–171.MATHGoogle Scholar
  41. Goldberg, D. E., 1989b, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA.MATHGoogle Scholar
  42. Goldberg, D. E., 1989c, Sizing populations for serial and parallel genetic algorithms, in: Proc. 3rd Int. Conf. on Genetic Algorithms, pp. 70–79.Google Scholar
  43. Goldberg, D. E., 1999a, The race, the hurdle, and the sweet spot: Lessons from genetic algorithms for the automation of design innovation and creativity, in: Evolutionary Design by Computers, P. Bentley, ed., chapter 4, pp. 105–118, Morgan Kaufmann, San Mateo, CA.Google Scholar
  44. Goldberg, D. E., 1999b, Using time efficiently: Genetic-evolutionary algorithms and the continuation problem, in: Proc. Genetic and Evolutionary Computation Conf., pp. 212–219.Google Scholar
  45. Goldberg, D. E., 2002, Design of Innovation: Lessons From and For Competent Genetic Algorithms, Kluwer, Boston, MA.MATHGoogle Scholar
  46. Goldberg, D. E. and Deb, K., 1991, A comparative analysis of selection schemes used in genetic algorithms, Foundations of Genetic Algorithms, G. J. E. Rawlins, ed., pp. 69–93.Google Scholar
  47. Goldberg, D. E., Deb, K. and Clark, J. H., 1992a, Genetic algorithms, noise, and the sizing of populations, Complex Syst. 6:333–362.MATHGoogle Scholar
  48. Goldberg, D. E., Deb, K. and Horn, J., 1992b, Massive multimodality, deception, and genetic algorithms, Parallel Problem Solving from Nature II, pp. 37–46, Elsevier, New York.Google Scholar
  49. Goldberg, D. E., Deb, K., Kargupta, H. and Harik, G., 1993, Rapid, accurate optimization of difficult problems using fast messy genetic algorithms, in: Proc. Int. Conf on Genetic Algorithms, pp. 56–64.Google Scholar
  50. Goldberg, D. E., Korb, B. and Deb, K., 1989, Messy genetic algorithms: Motivation, analysis, and first results. Complex Syst. 3:493–530.MATHMathSciNetGoogle Scholar
  51. Goldberg, D. E. and Lingle, R., 1985, Alleles, loci, and the TSP, in: Proc. 1st Int. Conf. on Genetic Algorithms, pp. 154–159.Google Scholar
  52. Goldberg, D. E. and Rudnick, M., 1991, Genetic algorithms and the variance of fitness, Complex Syst. 5:265–278.MATHGoogle Scholar
  53. Goldberg, D. E. and Sastry, K., 2001, A practical schema theorem for genetic algorithm design and tuning, in: Proc. of the Genetic and Evolutionary Computation Conf., pp. 328–335.Google Scholar
  54. Goldberg, D. E., Sastry, K. and Latoza, T., 2001, On the supply of building blocks, in: Proc. of the Genetic and Evolutionary Computation Conf., pp. 336–342.Google Scholar
  55. Goldberg, D. E. and Segrest, P., 1987, Finite Markov chain analysis of genetic algorithms, in: Proc. 2nd Int. Conf. on Genetic Algorithms, pp. 1–8.Google Scholar
  56. Goldberg, D. E. and Voessner, S., 1999, Optimizing global-local search hybrids, in: Proc. of the Genetic and Evolutionary Computation Conf., pp. 220–228.Google Scholar
  57. Gorges-Schleuter, M., 1989, ASPARAGOS: An asynchronous parallel genetic optimization strategy, in: Proc. 3rd Int. Conf. on Genetic Algorithms, pp. 422–428.Google Scholar
  58. Gorges-Schleuter, M., 1997, ASPARAGOS96 and the traveling salesman problem, in: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 171–174.Google Scholar
  59. Grefenstette, J. J., 1981, Parallel adaptive algorithms for function optimization, Technical Report No. CS-81-19, Computer Science Department, Vanderbilt University, Nashville, TN.Google Scholar
  60. Grefenstette, J. J. and Baker, J. E., 1989, How genetic algorithms work: A critical look at implicit parallelism, in: Proc. 3rd Int. Conf. on Genetic Algorithms, pp. 20–27.Google Scholar
  61. Grefenstette, J. J. and Fitzpatrick, J. M., 1985, Genetic search with approximate function evaluations, in: Proc. Int. Conf. on Genetic Algorithms and Their Applications, pp. 112–120.Google Scholar
  62. Harik, G. R., 1997, Learning linkage to eficiently solve problems of bounded difficulty using genetic algorithms, Doctoral Dissertation, University of Michigan, Ann Arbor, MI.Google Scholar
  63. Harik, G., 1999, Linkage learning via probabilistic modeling in the ECGA, IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, Urbana, IL.Google Scholar
  64. Harik, G., Cantú-Paz, E., Goldberg, D. E. and Miller, B. L., 1999, The gambler’s ruin problem, genetic algorithms, and the sizing of populations, Evol. Comput. 7:231–253.Google Scholar
  65. Harik, G. and Goldberg, D. E., 1997, Learning linkage, Foundations of Genetic Algorithms, 4:247–262.Google Scholar
  66. Harik, G., Lobo, F. and Goldberg, D. E., 1998, The compact genetic algorithm, in: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 523–528.Google Scholar
  67. Hart, W. E. and Belew, R. K., 1996, Optimization with genetic algorithm hybrids using local search, in: Adaptive Individuals in Evolving Populations, R. K. Belew, and M. Mitchell, eds, pp. 483–494, Addison-Wesley, Reading, MA.Google Scholar
  68. Hart, W., Krasnogor, N. and Smith, J. E. (eds), 2004, Special issue on memetic algorithms, Evol. Comput. 12 No. 3.Google Scholar
  69. Heckendorn, R. B. and Wright, A. H., 2004, Efficient linkage discovery by limited probing, Evol. Comput. 12:517–545.CrossRefGoogle Scholar
  70. Holland, J. H., 1975, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.Google Scholar
  71. Ibaraki, T., 1997, Combinations with other optimization methods, in: Handbook of Evolutionary Computation, T. Bäck, D. B. Fogel, and Z. Michalewicz, eds, pp. D3:1–D3:2, Institute of Physics Publishing and Oxford University Press, Bristol and New York.Google Scholar
  72. Jin, Y., 2003, A comprehensive survey of fitness approximation in evolutionary computation, Soft Comput. J. (in press).Google Scholar
  73. Kargupta, H., 1996, The gene expression messy genetic algorithm, in: Proc. Int. Conf. on Evolutionary Computation, pp. 814–819.Google Scholar
  74. Krasnogor, N., Hart, W. and Smith, J. (eds), 2004, Recent Advances in Memetic Algorithms, Studies in Fuzziness and Soft Computing, Vol. 166, Springer, Berlin.Google Scholar
  75. Krasnogor, N. and Smith, J. E., 2005, A tutorial for competent memetic algorithms: model, taxonomy and design issues, IEEE Trans. Evol. Comput., accepted for publication.Google Scholar
  76. Louis, S. J. and McDonnell, J., 2004, Learning with case injected genetic algorithms, IEEE Trans. Evol. Comput. 8:316–328.CrossRefGoogle Scholar
  77. Larrañaga, P. and Lozano, J. A. (eds), 2002, Estimation of Distribution Algorithms, Kluwer, Boston, MA.MATHGoogle Scholar
  78. Lin, S.-C., Goodman, E. D. and Punch, W. F., 1997, Investigating parallel genetic algorithms on job shop scheduling problem, 6th Int. Conf. on Evolutionary Programming, pp. 383–393.Google Scholar
  79. Man, K. F., Tang, K. S. and Kwong, S., 1999, Genetic Algorithms: Concepts and Design, Springer, London.MATHGoogle Scholar
  80. Manderick, B. and Spiessens, P., 1989, Fine-grained parallel genetic algorithms, in: Proc. 3rd Int. Conf. on Genetic Algorithms, pp. 428–433.Google Scholar
  81. Memetic Algorithms Home Page: http://www.densis.fee.unicamp.br/~moscato/memetic
  82. Michalewicz, Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn, Springer, Berlin.MATHGoogle Scholar
  83. Miller, B. L. and Goldberg, D. E., 1995, Genetic algorithms, tournament selection, and the effects of noise, Complex Syst. 9:193–212.MathSciNetGoogle Scholar
  84. Miller, B. L. and Goldberg, D. E., 1996a, Genetic algorithms, selection schemes, and the varying effects of noise, Evol. Comput. 4:113–131.Google Scholar
  85. Miller, B. L. and Goldberg, D. E., 1996b, Optimal sampling for genetic algorithms, Intelligent Engineering Systems through Artificial Neural Networks (ANNIE’96), Vol. 6, pp. 291–297, ASME Press, New York.Google Scholar
  86. Mitchell, M., 1996, Introduction to Genetic Algorithms, MIT Press, Boston, MA.Google Scholar
  87. Moscato, P., 1989, On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Technical Report C3P 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, CA.Google Scholar
  88. Moscato, P., 1999, Part 4: Memetic algorithms, in: New Ideas in Optimization, D. Corne, M. Dorigo and F. Glover, eds, pp. 217–294, McGraw-Hill, New York.Google Scholar
  89. Moscato, P., 2001, Memetic algorithms, in: Handbook of Applied Optimization, Section 3.6.4, P. M. Pardalos and M. G. C. Resende, eds, Oxford University Press, Oxford.Google Scholar
  90. Moscato, P. and Cotta, C., 2003, A gentle introduction to memetic algorithms, in: Handbook of Metaheuristics, F. Glover and G. Kochenberger, eds, Chapter 5, Kluwer, Norwell, MA.Google Scholar
  91. Mühlenbein, H. and Paaß, G., 1996, From recombination of genes to the estimation of distributions I. Binary parameters, in: Parallel Problem Solving from Nature IV, Lecture Notes in Computer Science, Vol. 1141, Springer, Berlin.Google Scholar
  92. Mühlenbein, H. and Schlierkamp-Voosen, D., 1993, Predictive models for the breeder genetic algorithm: I. continous parameter optimization, Evol. Comput. 1:25–49.Google Scholar
  93. Munetomo, M. and Goldberg, D. E., 1999, Linkage identification by non-monotonicity detection for overlapping functions, Evol. Comput. 7:377–398.Google Scholar
  94. Oliver, J. M., Smith, D. J. and Holland, J. R. C., 1987, A study of permutation crossover operators on the travelling salesman problem, in: Proc. 2nd Int. Conf. on Genetic Algorithms, pp. 224–230.Google Scholar
  95. Paechter, B., Cumming, A., Norman, M. G. and Luchian, H., 1996, Extensions to a memetic timetabling system, The Practice and Theory of Automated Timetabling I, E. K. Burke and P. Ross, eds, Lecture Notes in Computer Science, Vol. 1153, Springer, Berlin, pp. 251–265.Google Scholar
  96. Paechter, B., Cumming, A. and Luchian, H., 1995, The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms, Evolutionary Computing: AISB Workshop 1995, T. Fogarty, ed., Lecture Notes in Computer Science, Vol. 993, Springer, Berlin, pp. 86–93.Google Scholar
  97. Pelikan, M., 2005, Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithm, Springer, Berlin.MATHGoogle Scholar
  98. Pelikan, M. and Goldberg, D. E., 2001, Escaping hierarchical traps with competent genetic algorithms, in: Proc. Genetic and Evolutionary Computation Conf., pp. 511–518.Google Scholar
  99. Pelikan, M., Goldberg, D. E. and Cantú-Paz, E., 2000, Linkage learning, estimation distribution, and Bayesian networks, Evol. Comput. 8:314–341.CrossRefGoogle Scholar
  100. Pelikan, M., Lobo, F. and Goldberg, D. E., 2002, A survey of optimization by building and using probabilistic models, Comput. Optim. Appl. 21:5–20.MATHCrossRefMathSciNetGoogle Scholar
  101. Pelikan, M. and Sastry, K., 2004, Fitness inheritance in the Bayesian optimization algorithm, in: Proc. Genetic and Evolutionary Computation Conference, Vol. 2, pp. 48–59.Google Scholar
  102. Pettey, C. C, Leuze, M. R. and Grefenstette, J. J., 1987, A parallel genetic algorithm, in: Proc. 2nd Int. Conf. on Genetic Algorithms, pp. 155–161.Google Scholar
  103. Radcliffe, N. J. and Surry, P. D., 1994, Formal memetic algorithms, Evolutionary Computing: AISB Workshop 1994, T. Fogarty, ed., Lecture Notes in Computer Science, Vol. 865, pp. 1–16, Springer, Berlin.Google Scholar
  104. Reeves, C. R., 1995, Genetic algorithms, in: Modern Heuristic Techniques for Combinatorial Problems, C. R. Reeves, ed., McGraw-Hill, New York.Google Scholar
  105. Robertson, G. G., 1987, Parallel implementation of genetic algorithms in a classifier system, in: Proc. 2nd Int. Conf. on Genetic Algorithms, pp. 140–147.Google Scholar
  106. Rothlauf, F., 2002, Representations for Genetic and Evolutionary Algorithms, Springer, Berlin.MATHGoogle Scholar
  107. Rudolph, G., 2000, Takeover times and probabilities of non-generational selection rules, in: Proc. Genetic and Evolutionary Computation Conf., pp. 903–910.Google Scholar
  108. Sakamoto, Y. and Goldberg, D. E., 1997, Takeover time in a noisy environment, in: Proc. 7th Int. Conf. on Genetic Algorithms, pp. 160–165.Google Scholar
  109. Sastry, K., 2001, Evaluation-relaxation schemes for genetic and evolutionary algorithms, Master’s Thesis, General Engineering Department, University of Illinois at Urbana-Champaign, Urbana, IL.Google Scholar
  110. Sastry, K. and Goldberg, D. E., 2002, Analysis of mixing in genetic algorithms: A survey, IlliGAL Report No. 2002012, University of Illinois at Urbana-Champaign, Urbana, IL.Google Scholar
  111. Sastry, K. and Goldberg, D. E., 2003, Scalability of selectorecombinative genetic algorithms for problems with tight linkage, in: Proc. 2003 Genetic and Evolutionary Computation Conf., pp. 1332–1344.Google Scholar
  112. Sastry, K. and Goldberg, D. E., 2004a, Designing competent mutation operators via probabilistic model building of neighborhoods, in: Proc. 2004 Genetic and Evolutionary Computation Conference II, Lecture Notes in Computer Science, Vol. 3103, Springer, Berlin, pp. 114–125.Google Scholar
  113. Sastry, K. and Goldberg, D. E., 2004b, Let’s get ready to rumble: Crossover versus mutation head to head, in: Proc. 2004 Genetic and Evolutionary Computation Conf. II, Lecture Notes in Computer Science, Vol. 3103, Springer, Berlin, pp. 126–137.Google Scholar
  114. Sastry, K., Goldberg, D. E., & Pelikan, M., 2001, Don’t evaluate, inherit, in: Proc. Genetic and Evolutionary Computation Conf., pp. 551–558.Google Scholar
  115. Sastry, K., Pelikan, M. and Goldberg, D. E., 2004, Efficiency enhancement of genetic algorithms building-block-wise fitness estimation, in: Proc. IEEE Int. Congress on Evolutionary Computation, pp. 720–727.Google Scholar
  116. Smith, R., Dike, B. and Stegmann, S., 1995, Fitness inheritance in genetic algorithms, in: Proc. ACM Symp. on Applied Computing, pp. 345–350, ACM, New York.Google Scholar
  117. Spears, W., 1997, Recombination parameters, in: The Handbook of Evolutionary Computation, T. Bäck, D. B. Fogel and Z. Michalewicz, eds, Chapter E1.3, IOP Publishing and Oxford University Press, Philadelphia, PA, pp. E1.3:1–E1.3:13.Google Scholar
  118. Spears, W. M. and De Jong, K. A., 1994, On the virtues of parameterized uniform crossover, in: Proc. 4th Int. Conf. on Genetic Algorithms.Google Scholar
  119. Srivastava, R. and Goldberg, D. E., 2001, Verification of the theory of genetic and evolutionary continuation, in: Proc. Genetic and Evolutionary Computation Conf., pp. 551–558.Google Scholar
  120. Syswerda, G., 1989, Uniform crossover in genetic algorithms, in: Proc. 3rd Int. Conf. on Genetic Algorithms, pp. 2–9.Google Scholar
  121. Thierens, D., 1999, Scalability problems of simple genetic algorithms, Evol. Comput. 7:331–352.Google Scholar
  122. Thierens, D. and Goldberg, D. E., 1994a, Convergence models of genetic algorithm selection schemes, in: Parallel Problem Solving from Nature III, pp. 116–121.Google Scholar
  123. Thierens, D. and Goldberg, D. E., 1994b, Elitist recombination: An integrated selection recombination GA, in: Proc. 1st IEEE Conf. on Evolutionary Computation, pp. 508–512.Google Scholar
  124. Thierens, D., Goldberg, D. E. and Pereira, A. G., 1998, Domino convergence, drift, and the temporal-salience structure of problems, in: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 535–540.Google Scholar
  125. Valenzuala, J. and Smith, A. E., 2002, A seeded memetic algorithm for large unit commitment problems, J. Heuristics, 8:173–196.CrossRefGoogle Scholar
  126. Voigt, H.-M., Mühlenbein, H. and Schlierkamp-Voosen, D., 1996, The response to selection equation for skew fitness distributions, in: Proc. Int. Conf. on Evolutionary Computation, pp. 820–825.Google Scholar
  127. Watson, J. P., Rana, S., Whitely, L. D. and Howe, A. E., 1999, The impact of approximate evaluation on the performance of search algorithms for ware-house scheduling, J. Scheduling, 2:79–98.MATHCrossRefGoogle Scholar
  128. Whitley, D., 1995, Modeling hybrid genetic algorithms, in Genetic Algorithms in Engineering and Computer Science, G. Winter, J. Periaux, M. Galan and P. Cuesta, eds, Wiley, New York, pp. 191–201.Google Scholar
  129. Yu, T.-L., Goldberg, D. E., Yassine, A. and Chen, Y.-P., 2003, A genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm, Artificial Neural Networks in Engineering (ANNIE 2003), pp. 327–332.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2005

Authors and Affiliations

  • Kumara Sastry
    • 1
  • David Goldberg
    • 1
  • Graham Kendall
    • 2
  1. 1.University of IllinoisUSA
  2. 2.University of NottinghamUK

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