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Mutate large, but inherit small! On the analysis of rescaled mutations in (\(\tilde 1,\tilde \lambda\))-ES with noisy fitness data

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

The paper presents the asymptotical analysis of a technique for improving the convergence of evolution strategies (ES) on noisy fitness data. This technique that may be called “Mutate large, but inherit small”, is discussed in light of the EPP (evolutionary progress principle). The derivation of the progress rate formula is sketched, its predictions are compared with experiments, and its limitations are shown. The dynamical behavior of the ES is investigated. It will be shown that standard self-adaptation has considerable problems to drive the ES in its optimum working regime. Remedies are provided to improve the self-adaptation.

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References

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Authors and Affiliations

  1. Department of Computer Science XI, University of Dortmund, D-44221, Dortmund, Germany

    Hans-Georg Beyer

Authors
  1. Hans-Georg Beyer
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Beyer, HG. (1998). Mutate large, but inherit small! On the analysis of rescaled mutations in (\(\tilde 1,\tilde \lambda\))-ES with noisy fitness data. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056854

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  • DOI: https://doi.org/10.1007/BFb0056854

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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