Advertisement

Reducing Random Fluctuations in Mutative Self-adaptation

  • Thomas Philip Runarsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

A simple method of reducing random fluctuations experienced in step-size control under mutative self-adaptation is discussed. The approach taken does not require collective learning from the population, i.e. no recombination. It also does not require knowledge about the instantiations of the actual random mutation performed on the object variables. The method presented may be interpreted as an exponential recency-weighted average of trial strategy parameters sampled by a lineage.

Keywords

Sphere Model Strategy Parameter Object Variable Collective Learning Canonical Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    H.-G. Beyer. Toward a theory of evolution strategies: self-adaptation. Evolutionary Computation, 3(3):311–347, 1996.CrossRefGoogle Scholar
  2. 2.
    H.-G. Beyer. Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 186(2–4):239–267, 2000.zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    H.-G. Beyer. The Theory of Evolution Strategies. Springer-Verlag, Berlin, 2001.Google Scholar
  4. 4.
    N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution stategies. Evolutionary Computation, 2(9):159–195, 2001.CrossRefGoogle Scholar
  5. 5.
    A. Ostermeier, A. Gawelczyk, and N. Hansen. A derandomized approach to selfadaptation of evolution strategies. Evolutionary Computation, 2(4):369–380, 1995.CrossRefGoogle Scholar
  6. 6.
    I. Rechenberg. Evolutionstrategie’94. Frommann-Holzboog, Stuttgart, 1994.Google Scholar
  7. 7.
    T. P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3):284–294, September 2000.CrossRefGoogle Scholar
  8. 8.
    H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New-York, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Thomas Philip Runarsson
    • 1
  1. 1.Science InstituteUniversity of IcelandIceland

Personalised recommendations