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Resampling and its avoidance in genetic algorithms

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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 algorithms are widely used as optimization and adaptation tools, and they became important in artificial intelligence. Even though several successful applications have been reported, recent research has identified some inefficiencies in genetic algorithm performance. This paper argues that the degradation of genetic algorithm performance originates from the random application of the variation operators, since resampling of already visited points is not avoided. Consequently, this paper proposes an algorithmic framework, the “deterministic” genetic algorithm, that yields significantly faster convergence.

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Authors and Affiliations

  1. Department of Computer Science, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

    Ralf Salomon

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  1. Ralf Salomon
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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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Salomon, R. (1998). Resampling and its avoidance in genetic algorithms. 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/BFb0040786

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  • DOI: https://doi.org/10.1007/BFb0040786

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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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