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A Weighted Local Sharing Technique for Multimodal Optimisation

  • Grant Dick
  • Peter A. Whigham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing, spatially-structured populations and elitist replacement. In local sharing the bias toward sharing or spatial effect is controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect, deme sizes must be large, but the opposite must be true if one wishes to maximise the influence of spatial population structure. This paper introduces a modification to the local sharing method whereby parent selection and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect of fitness sharing is weighted according to the distances between individuals in the population structure. The proposed method, as tested on several benchmark problems, demonstrates a level of efficiency and parameter robustness that surpasses the basic local sharing method.

Keywords

Genetic Algorithm Parent Selection Local Sharing Multimodal Problem Multimodal Optimisation 
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 2008

Authors and Affiliations

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

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