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Spatially-Structured Sharing Technique for Multimodal Problems

Grant Dick and Peter Whigham   

  1. Department of Information Science, University of Otago, Dunedin, New Zealand
  • Revised:2007-11-27 Online:2008-01-15 Published:2008-01-10

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 {\it 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).

Key words: conformance testing; distributed system; CEBE; concurrent TTCN;

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