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Genetic and Evolutionary Computation Conference

GECCO 2003: Genetic and Evolutionary Computation — GECCO 2003 pp 344–355Cite as

Finite Population Models of Co-evolution and Their Application to Haploidy versus Diploidy

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

Abstract

In order to study genetic algorithms in co-evolutionary environments, we construct a Markov model of co-evolution of populations with fixed, finite population sizes. In this combined Markov model, the behavior toward the limit can be utilized to study the relative performance of the algorithms. As an application of the model, we perform an analysis of the relative performance of haploid versus diploid genetic algorithms in the co-evolutionary setup, under several parameter settings. Because of the use of Markov chains, this paper provides exact stochastic results on the expected performance of haploid and diploid algorithms in the proposed co-evolutionary model.

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Technische Universiteit Eindhoven, P.O. Box 513, 5600MB, Eindhoven, The Netherlands

    Anthony M. L. Liekens, Huub M. M. ten Eikelder & Peter A. J. Hilbers

Authors
  1. Anthony M. L. Liekens
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  2. Huub M. M. ten Eikelder
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  3. Peter A. J. Hilbers
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Liekens, A.M.L., ten Eikelder, H.M.M., Hilbers, P.A.J. (2003). Finite Population Models of Co-evolution and Their Application to Haploidy versus Diploidy. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_40

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  • DOI: https://doi.org/10.1007/3-540-45105-6_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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