Skip to main content

Reducing Random Fluctuations in Mutative Self-adaptation

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN VII (PPSN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H.-G. Beyer. Toward a theory of evolution strategies: self-adaptation. Evolutionary Computation, 3(3):311–347, 1996.

    Article  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  3. H.-G. Beyer. The Theory of Evolution Strategies. Springer-Verlag, Berlin, 2001.

    Google Scholar 

  4. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution stategies. Evolutionary Computation, 2(9):159–195, 2001.

    Article  Google Scholar 

  5. A. Ostermeier, A. Gawelczyk, and N. Hansen. A derandomized approach to selfadaptation of evolution strategies. Evolutionary Computation, 2(4):369–380, 1995.

    Article  Google Scholar 

  6. I. Rechenberg. Evolutionstrategie’94. Frommann-Holzboog, Stuttgart, 1994.

    Google Scholar 

  7. T. P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3):284–294, September 2000.

    Article  Google Scholar 

  8. H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New-York, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Runarsson, T.P. (2002). Reducing Random Fluctuations in Mutative Self-adaptation. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-45712-7_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics