Niche Radius Adaptation with Asymmetric Sharing

  • Vincent van der Goes
  • Ofer M. Shir
  • Thomas Bäck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


In the field of Genetic Algorithms, niching techniques have been invented with the aim to induce speciation on multimodal fitness landscapes. Unfortunately, they often rely on a problem-dependent niche radius parameter. This is the niche radius problem. In recent research, the possibilities to transfer niching techniques to the field of Evolution Strategies (ES) have been studied. First attempts were carried out to learn a good value for the niche radius through self-adaptation. In this paper we introduce a new niching method for ES with self-adaptation of the niche radius: asymmetric sharing. It is a form of fitness sharing. In contrast to earlier studies, it does not depend on coupling the niche radius to other strategy parameters. Experimental results indicate that asymmetric sharing performs well in comparison to traditional sharing, without relying on problem-dependent parameters.


Evolution Strategy Strategy Parameter Tournament Selection Evolution Strategy Niching Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rechenberg, I.: Evolutions strategies: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart (1973)Google Scholar
  2. 2.
    Schwefel, H.-P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981)zbMATHGoogle Scholar
  3. 3.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)zbMATHGoogle Scholar
  4. 4.
    Beyer, H.-G., Schwefel, H.-P.: Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing 1(1), 3–52 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Hansen, N.: Verallgemeinerte individuelle Schritweiteregelung in der Evolutionsstrategie. PhD thesis, Technical University of Berlin (1998)Google Scholar
  6. 6.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor (1975)Google Scholar
  7. 7.
    de Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan (1975)Google Scholar
  8. 8.
    Goldberg, D.E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. In: Proceedings of ICGA 1987, pp. 42–50. Morgan Kaufmann, San Francisco (1987)Google Scholar
  9. 9.
    Deb, K., Goldberg, D.E.: An Investigation of Niche and Species Formation in Genetic Function Optimization. In: Proceedings of ICGA 1989, pp. 42–50. Morgan Kaufmann, San Mateo (1989)Google Scholar
  10. 10.
    Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament Selection, Niching, and the Preservation of Diversity. IlliGAL Report 91011, University of Illinois at Urbana-Champaign (1991)Google Scholar
  11. 11.
    Yin, X., Germay, N.: Improving Genetic Algorithms with Sharing through Cluster Analysis. In: Proceedings of ICGA 1993, pp. 100–101. Morgan Kaufmann, San Francisco (1993)Google Scholar
  12. 12.
    Mahfoud, S.: Niching Methods for Genetic Algorithms. PhD thesis, University of Illinois at Urbana Champaign (1995)Google Scholar
  13. 13.
    Miller, B., Shaw., M.: Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization. In: ICEC 1996 Proceedings, pp. 786–791. IEEE Press, New York (1996)Google Scholar
  14. 14.
    Goldberg, D.E., Wang, L.: Adaptive Niching via Coevolutionary Sharing. In: Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, pp. 21–38. John Wiley & Sons Ltd, West Sussex (1997)Google Scholar
  15. 15.
    Oosten, M., van der Goes, V.: Niching in Evolution Strategies, Technical Report, University of Leiden (2004)Google Scholar
  16. 16.
    Shir, O.M., Bäck, T.: Dynamic Niching in Evolution Strategies with Covariance Matrix Adaptation. In: CEC 2005 Proceedings, pp. 2584–2591. IEEE, Piscataway (2005)Google Scholar
  17. 17.
    Shir, O.M., Bäck, T.: Niche Radius Adaptation in the CMA-ES Niching Algorithm. In: PPSN 2006 Proceedings, pp. 142–151. Springer, Heidelberg (2006)Google Scholar
  18. 18.
    Shir, O.M., Emmerich, M., Bäck, T.: Self-Adaptive Niching CMA-ES with Mahalanobis Metric. In: CEC 2007 Proceedings, pp. 820–827. IEEE Press, Singapore (2007)Google Scholar
  19. 19.
    Törn, A., Zilinskas, A.: Global Optimization, vol. 350. Springer, Heidelberg (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vincent van der Goes
    • 1
  • Ofer M. Shir
    • 1
  • Thomas Bäck
    • 1
    • 2
  1. 1.Leiden Institute of Advanced Computer ScienceUniversiteit LeidenLeidenThe Netherlands
  2. 2.NuTech SolutionsDortmundGermany

Personalised recommendations