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Spatially-Structured Evolutionary Algorithms and Sharing: Do They Mix?

  • Grant Dick
  • Peter A. Whigham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm (EA). However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone (and vice-versa).

Keywords

Genetic Algorithm Elitism Strategy Local Sharing Fitness Sharing 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Grant Dick
    • 1
  • Peter A. Whigham
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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