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GA-Optimal fitness functions

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Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

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Abstract

Genetic algorihtms (GA) are supposed to suceed through the use of ‘implicit parallelism’ and ‘building blocks’. Given these properties, we can create rules for constructing GA-optimal fitness functions. Some of these rules are also relevant to evolutionary programming and evolution strategies searches. The goal is to help the practitioner develop an intuition about when GA are effective.

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Authors and Affiliations

  1. Center for Adaptive Systems Applications, Inc., 1302 Osage, Suite A, 87505, Santa Fe, NM

    Joseph L. Breeden

Authors
  1. Joseph L. Breeden
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Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Breeden, J.L. (1998). GA-Optimal fitness functions. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040763

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  • DOI: https://doi.org/10.1007/BFb0040763

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

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