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Maximum entropy analysis of genetic algorithm operators

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Evolutionary Computing (AISB EC 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 993))

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Abstract

A maximum entropy approach is used to derive a set of equations describing the evolution of a genetic algorithm involving crossover, mutation and selection. The problem is formulated in terms of cumulants of the fitness distribution. Applying this method to very simple problems, the dynamics of the genetic algorithm can be reduced to a set of nonlinear coupled difference equations which give good results when truncated to four variables. These equations correctly predict the best fitness after convergence as a function of the genetic search parameters. Suggestions concerning annealing rates for time-dependent search parameters are also discussed.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manchester, M13 9PL, Manchester, UK

    Jonathan L. Shapiro

  2. NORDITA, Blegdamsvej 17, DK-2100, Copenhagen Ø, Denmark

    Adam Prügel-Bennett

Authors
  1. Jonathan L. Shapiro
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  2. Adam Prügel-Bennett
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Editor information

Terence C. Fogarty

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© 1995 Springer-Verlag Berlin Heidelberg

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Shapiro, J.L., Prügel-Bennett, A. (1995). Maximum entropy analysis of genetic algorithm operators. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_21

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  • DOI: https://doi.org/10.1007/3-540-60469-3_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60469-3

  • Online ISBN: 978-3-540-47515-6

  • eBook Packages: Springer Book Archive

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