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Spatially-Structured Sharing Technique for Multimodal Problems

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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).

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  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Grant Dick & Peter Whigham

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Correspondence to Grant Dick.

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Dick, G., Whigham, P. Spatially-Structured Sharing Technique for Multimodal Problems. J. Comput. Sci. Technol. 23, 64–76 (2008). https://doi.org/10.1007/s11390-008-9110-6

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