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Fast evolution strategies

  • Issues in Evolutionary Optimization
  • Conference paper
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Evolutionary Programming VI (EP 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1213))

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Abstract

Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming

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Author information

Authors and Affiliations

  1. Computational Intelligence Group, School of Computer Science, University College, The University of New South Wales Australian Defence Force Academy, 2600, Canberra, ACT, Australia

    Xin Yao & Yong Liu

Authors
  1. Xin Yao
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  2. Yong Liu
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Editor information

Peter J. Angeline Robert G. Reynolds John R. McDonnell Russ Eberhart

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© 1997 Springer-Verlag Berlin Heidelberg

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Yao, X., Liu, Y. (1997). Fast evolution strategies. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014808

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  • DOI: https://doi.org/10.1007/BFb0014808

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62788-3

  • Online ISBN: 978-3-540-68518-0

  • eBook Packages: Springer Book Archive

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