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)


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).


Genetic Algorithm Elitism Strategy Local Sharing Fitness Sharing Niching Method 


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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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