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Optimal Weighted Recombination

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Foundations of Genetic Algorithms (FOGA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3469))

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Abstract

Weighted recombination is a means for improving the local search performance of evolution strategies. It aims to make effective use of the information available, without significantly increasing computational costs per time step. In this paper, the potential speed-up resulting from using rank-based weighted recombination is investigated. Optimal weights are computed for the sphere model, and comparisons with the performance of strategies that do not make use of weighted recombination are presented. It is seen that unlike strategies that rely on unweighted recombination and truncation selection, weighted multirecombination evolution strategies are able to improve on the serial efficiency of the (1+1)-ES on the sphere. The implications of the use of weighted recombination for noisy optimization are studied, and parallels to the use of rescaled mutations are drawn. The cumulative step length adaptation mechanism is formulated for the case of an optimally weighted evolution strategy, and its performance is analyzed.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, B3H 1W5

    Dirk V. Arnold

Authors
  1. Dirk V. Arnold
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, University of Montana, 59812, Missoula, Montana, USA

    Alden H. Wright

  2. Computer Science Department, University of Tennessee, 37996, Knoxville, TN, USA

    Michael D. Vose

  3. Computer Science Department, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA

    Kenneth A. De Jong

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, Aizu-Wakamatsu City, Fukushima Prefecture, Japan

    Lothar M. Schmitt

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© 2005 Springer-Verlag Berlin Heidelberg

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Arnold, D.V. (2005). Optimal Weighted Recombination. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds) Foundations of Genetic Algorithms. FOGA 2005. Lecture Notes in Computer Science, vol 3469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11513575_12

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  • DOI: https://doi.org/10.1007/11513575_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27237-3

  • Online ISBN: 978-3-540-32035-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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