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Genetic Algorithms

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

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Abstract

Genetic algorithms (GAs) have become popular as a means of solving hard combinatorial optimization problems. The first part of this chapter briefly traces their history, explains the basic concepts and discusses some of their theoretical aspects. It also references a number of sources for further research into their applications. The second part concentrates on the detailed implementation of a GA. It discusses the fundamentals of encoding a ‘genotype’ in different circumstances and describes the mechanics of population selection and management and the choice of genetic ‘operators’ for generating new populations. In closing, some specific guidelines for using GAs in practice are provided.

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Notes

  1. 1.

    Well-meaning attempts to read off the validity or otherwise of Darwinism from the performance of GAs are illegitimate. GAs are clear examples of ‘intelligent design’.

  2. 2.

    The NFLT, put simply, says that on the average, nothing—ant colonies, GAs, simulated annealing, tabu search, etc.—is better than random search. Success comes from adapting the technique to the problem at hand, which of course implies some input of information from the researcher.

  3. 3.

    Apart from the intrinsic interest of these papers, it is well worth checking to see if someone has tried your bright new idea already!

  4. 4.

    Note that the purpose of SUS is not to reduce the total of random numbers needed. Having generated a multiset of size M as our ‘mating pool’, we still have to decide which pairs mate together, whereas in RWS we can simply pair them in the order generated.

  5. 5.

    There is a simple algorithm for doing this efficiently—see Nijenhuis and Wilf [111], for example, or look at the Stony Brook Algorithm Repository [112].

  6. 6.

    This phenomenon is a common one whenever the coding function \(c(\cdot)\) is not injective. It has been observed in problems ranging from optimizing neural nets to the TSP. Radcliffe, who calls it ‘degeneracy’ [125], has presented the most thorough analysis of this problem and how to treat it.

References

  1. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan; re-issued by MIT Press (1992) (1975)

    Google Scholar 

  2. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipen der biologischen Evolution. 2nd edn. 1993 Frommmann-Holzboog Verlag, Stuttgart (1973)

    Google Scholar 

  3. Schwefel, H-P.: Numerische Optimierung von Computer-modellen mittels der Evolutionsstrategie. Birkhäuser, Basel. (English edn. Numerical Optimization of Computer Models, John, Chichester, (1981) (1977)

    Google Scholar 

  4. Fogel, D.B.: Evolutionary Computation: The Fossil Record. IEEE Press, Piscataway, NJ (1998)

    Google Scholar 

  5. De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral dissertation, University of Michigan, Ann Arbor, Michigan (1975)

    Google Scholar 

  6. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Massachusetts (1989)

    Google Scholar 

  7. De Jong, K.A.: Genetic algorithms are NOT function optimizers. In: [Whitley, L.D. (ed.): Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, CA (1993)], pp. 5–18 (1993)

    Google Scholar 

  8. Lin, S.: Computer solutions of the traveling salesman problem. Bell Systems Tech. J. 44, pp. 2245–2269 (1965)

    Google Scholar 

  9. Roberts, S.M., Flores, B.: An engineering approach to the travelling salesman problem. Man. Sci. 13, 269–288 (1966)

    Google Scholar 

  10. Nugent, C.E., Vollman, T.E., Ruml, J.E.: An experimental comparison of techniques for the assignment of facilities to locations. Oper. Res. 16, 150–173 (1968)

    Google Scholar 

  11. Reeves, C.R.: Genetic algorithms for the Operations Researcher. INFORMS J Comput. 9, 231–250 (1997)

    Google Scholar 

  12. Reeves, C.R., Rowe, J.E.: Genetic Algorithms—Principles and Perspectives. Kluwer, Norwell, MA (2002)

    Google Scholar 

  13. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Google Scholar 

  14. Reeves, C.R., Wright, C.C.: Genetic algorithms and the design of experiments. In: Davis, L.D. De Jong, K.A. Vose, M.D., Whitley, L.D. (eds.) Evolutionary Algorithms: IMA Volumes in Mathematics and its Applications, vol. 111, 207–226 Springer, New York (1999)

    Google Scholar 

  15. Macready, W.G., Wolpert, D.H.: Bandit problems and the exploration/exploitation tradeoff. IEEE Trans. Evol. Comput. 2, 2–13 (1998)

    Google Scholar 

  16. Stephens, C.R., Zamora, A., Wright, A.H.: Perturbation theory and the renormalization group in genetic dynamics. In: [Wright, A.H., et al. (eds.): Foundations of Genetic Algorithms 8, LNCS 3469. Springer, Berlin (2005)], pp. 192–214 (2005)

    Google Scholar 

  17. Mitchell, M., Holland, J.H., Forrest, S.: When will a genetic algorithm outperform hill climbing? In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) (1994) Advances in Neural Information Processing Systems 6, Morgan Kaufmann, San Mateo, CA (1994)

    Google Scholar 

  18. Vose, M.D.: Modeling simple genetic algorithms. In [Whitley, L.D. (ed.): Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, CA (1993)], CA, 63–73 (1993)

    Google Scholar 

  19. Whitley, D.: An executable model of a simple genetic algorithm. In: [Whitley, L.D. (ed.): Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, CA (1993)], pp. 45–62 (1993)

    Google Scholar 

  20. Vose, M.D.: A closer look at mutation in genetic algorithms. Ann. Math. AI 10, 423–434 (1994)

    Google Scholar 

  21. Vose, M.D., Wright, A.H.: Stability of vertex fixed points and applications. In: [Whitley, D., Vose, M. (eds.): Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Mateo, CA (1995)], pp. 103–113 (1995)

    Google Scholar 

  22. De Jong, K.A., Spears, W.M., Gordon, D.F.: Using Markov chains to analyze GAFOs. In: [Whitley, D., Vose, M. (eds.): Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Mateo, CA (1995)], pp. 115–137 (1995)

    Google Scholar 

  23. Shapiro, J.L., Prügel-Bennett, A., Rattray, M.: A statistical mechanics formulation of the dynamics of genetic algorithms. Lecture Notes in Computer Science 65, Springer Berlin, 17–27 (1994)

    Google Scholar 

  24. Peck, C.C., Dhawan, A.P.: Genetic algorithms as global random search methods: An alternative perspective. Evolutionary Computation 3, 39–80 (1995)

    Google Scholar 

  25. Reeves, C.R.: Predictive measures for problem difficulty. In: Proceedings of 1999 Congress on Evolutionary Computation, IEEE Press, 736–743 (1999)

    Google Scholar 

  26. Reeves, C.R.: Genetic algorithms and neighbourhood search. In: Fogarty, T.C., (ed.) Evolutionary Computing: AISB Workshop, Leeds, UK, April 1994; Selected Papers, Springer, Berlin, 115–130 (1994)

    Google Scholar 

  27. Jones, T.C.: Evolutionary Algorithms, Fitness Landscapes and Search. Doctoral dissertation, University of New Mexico, Albuquerque, NM (1995)

    Google Scholar 

  28. Culberson, J.C.: Mutation-crossover isomorphisms and the construction of discriminating functions. Evol. Comput. 2, 279–311 (1995)

    Google Scholar 

  29. Stadler, P.F., Wagner, G.P.: Algebraic theory of recombination spaces. Evol. Comput. 5, 241–275 (1998)

    Google Scholar 

  30. Reidys, C.M., Stadler, P.F.: Combinatorial landscapes. SIAM Review 44, 3–54 (2002)

    Google Scholar 

  31. Reeves, C.R.: Fitness landscapes and evolutionary algorithms. In: Fonlupt, C., Hao, J-K., Lutton, E., Ronald, E., Schoenauer, M., (eds.) Artificial Evolution: 4th European Conference; Selected Papers. Springer, Berlin, 3–20 (2000)

    Google Scholar 

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

    Google Scholar 

  33. Chambers, L. (ed.): Practical Handbook of Genetic Algorithms: Applications, Volume I. CRC Press, Boca Raton, Florida (1995)

    Google Scholar 

  34. Chambers, L. (ed.): Practical Handbook of Genetic Algorithms: New Frontiers, Volume II. CRC Press, Boca Raton, Florida (1995)

    Google Scholar 

  35. Alander, J.T.: An Indexed Bibliography of Genetic Algorithms in Operations Research. ftp://garbo.uwasa.fi/cs/report94-1/gaORbib.pdf. last accessed 16 May 2009 (2008)

  36. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1996)

    Google Scholar 

  37. Reeves, C.R. (ed.): Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publications, Oxford, UK; re-issued by McGraw-Hill, London, UK (1995) (1993)

    Google Scholar 

  38. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)

    Google Scholar 

  39. Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  40. Bäck, Th.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)

    Google Scholar 

  41. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, Berlin (2007)

    Google Scholar 

  42. Spears, W.M.: Evolutionary Algorithms: the Role of Mutation and Recombination. Springer, New York (2000)

    Google Scholar 

  43. De Jong, K.A.: Evolutionary Computation. MIT Press, Cambridge, MA (2006)

    Google Scholar 

  44. Vose, M.D.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge, MA (1999)

    Google Scholar 

  45. Schmitt, L., Nehaniv, C.L., Fujii, R.H.: Linear analysis of genetic algorithms. Theor. Comput. Sci. 200, 101–134 (1998)

    Google Scholar 

  46. Schmitt, L.: Theory of genetic algorithms. Theor. Comput. Sci. 259, 1–61 (2001)

    Google Scholar 

  47. Grefenstette, J.J. (ed.): Proceedings of an International Conference on Genetic Algorithms and their applications. Lawrence Erlbaum Associates, Hillsdale, NJ (1985)

    Google Scholar 

  48. Grefenstette, J.J. (ed.): Proceedings of the 2nd International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, Hillsdale, NJ (1987)

    Google Scholar 

  49. Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)

    Google Scholar 

  50. Belew, R.K., Booker, L.B. (eds.): Proceedings of 4th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991)

    Google Scholar 

  51. Forrest, S. (ed.): Proceedings of 5th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1993)

    Google Scholar 

  52. Eshelman, L.J. (ed.): Proceedings of 6th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1995)

    Google Scholar 

  53. Bäck, Th. (ed.): Proceedings of 7th International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA (1997)

    Google Scholar 

  54. Angeline, P.J. (ed.): Proceedings of the 1999 Congress on Evolutionary Computation. IEEE Press, Piscataway, NJ (1999)

    Google Scholar 

  55. Zalzala, A. (ed.): Proceedings of the 2000 Congress on Evolutionary Computation. IEEE Press, Piscataway, NJ (2000)

    Google Scholar 

  56. Kim, J-H. (ed.): Proceedings of the 2001 Congress on Evolutionary Computation. IEEE Press, Piscataway, NJ (2001)

    Google Scholar 

  57. Banzhaf, W., et al. (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999). Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  58. Whitley, D., et al. (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000). Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  59. Spector, L., et al. (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001). Morgan Kaufmann, San Francisco, CA (2001)

    Google Scholar 

  60. Langdon, W.B., et al.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). Morgan Kaufmann, San Francisco, CA (2002)

    Google Scholar 

  61. Cantú-Paz, E., et al.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), LNCS 2723/2724. Springer, Berlin (2003)

    Google Scholar 

  62. Deb, K., et al. (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004), LNCS 3102/3103. Springer, Berlin (2004)

    Google Scholar 

  63. Beyer, H-G., et al.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005). ACM Press, New York (2005)

    Google Scholar 

  64. Cattolico, M. (ed.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006). ACM Press, New York (2006)

    Google Scholar 

  65. Lipson, H. (ed.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007). ACM Press, New York (2007)

    Google Scholar 

  66. Ryan, C., et al.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press (2008)

    Google Scholar 

  67. Schwefel, H-P., Männer, R. (eds.): Parallel Problem-Solving from Nature. Springer, Berlin (1991)

    Google Scholar 

  68. Männer, R., Manderick, B. (eds.): Parallel Problem-Solving from Nature, 2. Elsevier Science Publishers, Amsterdam (1992)

    Google Scholar 

  69. Davidor, Y., Schwefel, H-P., Männer, R. (eds.): Parallel Problem-Solving from Nature, 3. Springer, Berlin (1994)

    Google Scholar 

  70. Voigt, H-M., et al. (eds.): Parallel Problem-Solving from Nature, 4, LNCS 1141. Springer, Berlin (1996)

    Google Scholar 

  71. Eiben, A.E., et al. (eds.): Parallel Problem-Solving from Nature, 5, LNCS 1498. Springer, Berlin (1998)

    Google Scholar 

  72. Schoenauer, M., et al. (eds.): Parallel Problem-Solving from Nature, 6, LNCS 1917. Springer, Berlin (2000)

    Google Scholar 

  73. Merelo Guervs, J.J., et al. (eds.): Parallel Problem-Solving from Nature, 7, LNCS 2439. Springer, Berlin (2002)

    Google Scholar 

  74. Yao, X., et al. (eds.): Parallel Problem-Solving from Nature, 8, LNCS 3242. Springer, Berlin (2004)

    Google Scholar 

  75. Runarsson, T.P., et al. (eds.): Parallel Problem-Solving from Nature, 9, LNCS 4193. Springer, Berlin (2006)

    Google Scholar 

  76. Rudolph, G., et al.: Parallel Problem-Solving from Nature, 10, LNCS 5199. Springer, Berlin (2008)

    Google Scholar 

  77. Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds.): Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (1993)

    Google Scholar 

  78. Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds.): Proceedings of the 2nd International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (1995)

    Google Scholar 

  79. Smith, G.D., Steele, N.C., Albrecht, R.F. (eds.): Proceedings of the 3rd International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (1997)

    Google Scholar 

  80. Dobnikar, A., Steele, N.C., Pearson, D.W., Albrecht, R.F. (eds.): Proceedings of the 4th International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (1999)

    Google Scholar 

  81. Kůrková, V., Steele, N.C., Neruda, R., Kárný, M. (eds.): Proceedings of the 5th International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (2001)

    Google Scholar 

  82. Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds.): Proceedings of the 6th International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Vienna (2003)

    Google Scholar 

  83. Ribeiro, B., Albrecht, R.F., Steele, N.C., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.): Proceedings of the International Conference on Adaptive and Natural Computing Algorithms. Springer, Vienna (2005)

    Google Scholar 

  84. Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.): Adaptive and Natural Computing Algorithms; Proceedings of ICANNGA 2007, LNCS 4431/4432. Springer, Berlin (2007)

    Google Scholar 

  85. Rawlins, G.J.E. (ed.): Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991)

    Google Scholar 

  86. Whitley, L.D. (ed.): Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, CA (1993)

    Google Scholar 

  87. Whitley, D., Vose, M. (eds.): Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Mateo, CA (1995)

    Google Scholar 

  88. Belew, R.K., Vose, M.D. (eds.): Foundations of Genetic Algorithms 4. Morgan Kaufmann, San Francisco, CA (1997)

    Google Scholar 

  89. Banzhaf, W., Reeves, C.R. (eds.): Foundations of Genetic Algorithms 5. Morgan Kaufmann, San Francisco, CA (1999)

    Google Scholar 

  90. Martin, W.N., Spears, W.M. (eds.): Foundations of Genetic Algorithms 6. Morgan Kaufmann, San Francisco, CA (2001)

    Google Scholar 

  91. De Jong, K.A., Poli, R., Rowe, J.E. (eds.): Foundations of Genetic Algorithms 7. Morgan Kaufmann, San Francisco, CA (2003)

    Google Scholar 

  92. Wright, A.H., et al. (eds.): Foundations of Genetic Algorithms 8, LNCS 3469. Springer, Berlin (2005)

    Google Scholar 

  93. Stephens, C.R., et al. (eds.): Foundations of Genetic Algorithms 9, LNCS 4436. Springer, Berlin (2007)

    Google Scholar 

  94. Goldberg, D.E.: Optimal Initial Population Size for Binary-Coded Genetic Algorithms. TCGA Report 85001. University of Alabama, Tuscaloosa (1985)

    Google Scholar 

  95. Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 70–79 (1989)

    Google Scholar 

  96. Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE-SMC 16, 122–128 (1986)

    Google Scholar 

  97. Schaffer, J.D., Caruana, R.A., Eshelman, L.J., Das, R.: A study of control parameters affecting online performance of genetic algorithms for function optimization. In: [Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)], pp. 51–60 (1989)

    Google Scholar 

  98. Reeves, C.R.: Using genetic algorithms with small populations. In: [Forrest, S. (ed.): Proceedings of 5th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1993)], pp. 92–99 (1993)

    Google Scholar 

  99. Rees, J., Koehler, G.J.: An investigation of GA performance results for different cardinality alphabets. In: Davis, L.D., De Jong, K.A., Vose, M.D., Whitley, L.D. (eds.): Evolutionary Algorithms: IMA Volumes in Mathematics and its Applications, Vol. 111, Springer, New York, pp. 191–206 (1998) (1999)

    Google Scholar 

  100. Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22, 5–13 (1995)

    Google Scholar 

  101. Ahuja, R.K., Orlin, J.B.: Developing fitter GAs. INFORMS J. Comput. 9, 251–253 (1997)

    Google Scholar 

  102. Kapsalis, A., Smith, G.D., Rayward-Smith, V.J.: Solving the graphical steiner tree problem using genetic algorithms. J. Oper. Res. Soc. 44, 397–406 (1993)

    Google Scholar 

  103. Levine, D.: GAs: A practitioner’s view. INFORMS J. Comput. 9, 256–257 (1997)

    Google Scholar 

  104. Aytug, H., Koehler, G.J.: New stopping criterion for genetic algorithms. Eur. J. Oper. Res. 126, 662–674 (2000)

    Google Scholar 

  105. Greenhalgh, D., Marshall, S.: Convergence criteria for genetic algorithms. SIAM J. Comput. 30, 269–282 (2000)

    Google Scholar 

  106. Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: [Grefenstette, J.J. (ed.): Proceedings of the 2nd International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, Hillsdale, NJ (1987)], 14–21 (1987)

    Google Scholar 

  107. Lohr, S.L.: Sampling: Design and Analysis. Duxbury Press, Pacific Grove, CA (1999)

    Google Scholar 

  108. Hancock, P.J.B.: An empirical comparison of selection methods in evolutionary algorithms. In: Fogarty, T.C. (ed.) Evolutionary Computing: AISB Workshop, Leeds, UK, April 1994; Selected Papers, Springer, Berlin, pp. 80–94 (1994)

    Google Scholar 

  109. Whitley, D.: The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: [Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)], pp. 116–121 (1989)

    Google Scholar 

  110. Saliby, E.: Descriptive sampling: A better approach to Monte Carlo simulation. J. Oper. Res. Soc. 41, 1133–1142 (1990)

    Google Scholar 

  111. Nijenhuis, A., Wilf, H.S.: Combinatorial Algorithms for Computers and Calculators. Academic, New York (1978)

    Google Scholar 

  112. Skiena, S.S.: The Stony Brook Algorithm Repository. http://www.cs.sunysb.edu/~/major_section/1.3.shtml. last accessed 18 August 2010 (2000)

  113. Eshelman, L.J., Caruana, R.A., Schaffer, J.D.: Biases in the crossover landscape. In: [Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)], pp. 10–19 (1989)

    Google Scholar 

  114. Syswerda, G. Uniform crossover in genetic algorithms. In: [Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)], 2–9 (1989)

    Google Scholar 

  115. De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Ann. Math. AI 5, 1–26 (1992)

    Google Scholar 

  116. Booker, L.B.: Improving search in genetic algorithms. In: Davis, L. (ed.) (1987) Genetic Algorithms and Simulated Annealing. Morgan Kauffmann, Los Altos, CA, pp. 61–73 (1987)

    Google Scholar 

  117. Goldberg, D.E., Lingle, R.: Alleles, loci and the traveling salesman problem. In: [Grefenstette, J.J. (ed.): Proceedings of an International Conference on Genetic Algorithms and their applications. Lawrence Erlbaum Associates, Hillsdale, NJ (1985)], pp. 154–159 (1985)

    Google Scholar 

  118. Bremermann, H.J., Rogson, J., Salaff, S.: Global properties of evolution processes. In: Pattee, H.H. (ed.) Natural Automata and Useful Simulations, Spartan Books, Washington DC, pp. 3–42 (1966)

    Google Scholar 

  119. Fogel, D.B.: An overview of evolutionary programming. In: Davis, L.D., De Jong, K.A., Vose, M.D., Whitley, L.D. (eds.) Evolutionary Algorithms: IMA Volumes in Mathematics and its Applications, Vol. 111, Springer, New York, pp. 89–109 (1999)

    Google Scholar 

  120. Fogarty, T.C.: Varying the probability of mutation in the genetic algorithm. In: [Schaffer, J.D. (ed.): Proceedings of 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1989)], pp. 104–109 (1989)

    Google Scholar 

  121. Eiben, A.E., Schut, M.C., de Wilde, A.R.: Is self-adaptation of selection pressure and population size possible? A case study. In: [Runarsson, T.P., et al. (eds.): Parallel Problem-Solving from Nature, 9, LNCS 4193. Springer, Berlin (2006)], pp. 900–909 (2006)

    Google Scholar 

  122. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: [Rawlins, G.J.E. (ed.): Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991)], pp. 69–93 (1991)

    Google Scholar 

  123. Radcliffe, N.J., George, F.A.W.: A study in set recombination. In: [Forrest, S. (ed.): Proceedings of 5th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1993)], pp. 23–30 (1993)

    Google Scholar 

  124. McKee, S., Reed, M.B.: An algorithm for the alignment of gas turbine components in aircraft. IMA J. Math. Manag. 1, 133–144 (1987)

    Google Scholar 

  125. Radcliffe, N.J., Surry, P.D.: Formae and the variance of fitness. In: [Whitley, D., Vose, M. (eds.): Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Mateo, CA (1995)], pp. 51–72 (1995)

    Google Scholar 

  126. Fox, B.R., McMahon, M.B.: Genetic operators for sequencing problems. In: [Rawlins, G.J.E. (ed.): Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991)], pp. 284–300 (1991)

    Google Scholar 

  127. Poon, P.W., Carter, J.N.: Genetic algorithm crossover operators for ordering applications. Comput. Oper. Res. 22, 135–147 (1995)

    Google Scholar 

  128. Reeves, C.R., Yamada, T.: Genetic algorithms, path relinking and the flowshop sequencing problem. Evol. Comput. 6, 45–60 (1998)

    Google Scholar 

  129. Ross, P. srandom() anomaly. Genetic Algorithms Digest, http://www.aridolan.com/maillists/mlframes_ns.html. last accessed 18 August 2010, 11:23 (1997)

  130. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C : The Art of Scientific Computing. Cambridge University Press, Cambridge, UK (1992)

    Google Scholar 

  131. Reeves, C.R.: The ‘crossover landscape’ and the Hamming landscape for binary search spaces. In: [De Jong, K.A., Poli, R., Rowe, J.E. (eds.): Foundations of Genetic Algorithms 7. Morgan Kaufmann, San Francisco, CA (2003)], pp. 81–97 (2002)

    Google Scholar 

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  1. Department of Mathematics, Statistics and Engineering Science, Coventry University, Priory St, Coventry, UK

    Colin R. Reeves

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Reeves, C.R. (2010). Genetic Algorithms. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_5

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