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On the Design of Problem-specific Evolutionary Algorithms

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Advances in Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

In this chapter a set of guidelines for the design of genetic operators and the representation of the phenotype space in evolutionary algorithms (EAs) is proposed. These guidelines should help to systematize the design of problem-specific EAs by making the genetic operators behave in a controlled fashion with respect to metrics on geno- and phenotype space. Because we assume that we have enough domain knowledge to choose metrics that smooth the fitness landscape, this controlled behavior should improve the efficiency of the EA.

The applicability of this concept is shown by the systematic design of a genetic programming system for finding Boolean functions. This system is the first genetic programming system to have reportedly found the 12 parity function.

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Author information

Authors and Affiliations

  1. University of Dortmund FB Informatik, 44221, Dortmund, Germany

    Stefan Droste & Dirk Wiesmann

Authors
  1. Stefan Droste
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  2. Dirk Wiesmann
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Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700 035, India

    Ashish Ghosh

  2. Department of Management Information, Hannan University, 5-4-33 Amamhigigashi, Matsubara, Osaka, 580-8502, Japan

    Shigeyoshi Tsutsui

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© 2003 Springer-Verlag Berlin Heidelberg

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Droste, S., Wiesmann, D. (2003). On the Design of Problem-specific Evolutionary Algorithms. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62386-8

  • Online ISBN: 978-3-642-18965-4

  • eBook Packages: Springer Book Archive

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