Multimodal optimisation with structured populations and local environments

Authors:
Grant Dick;Peter A. Whigham
Affiliations:
Department of Information Science, University of Otago, Dunedin, New Zealand;Department of Information Science, University of Otago, Dunedin, New Zealand
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 6
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Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)

Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)

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Abstract

Spatially-structured evolutionary algorithms are frequently implemented using a homogeneous environment throughout space. Such a configuration does not promote local adaptation of individuals in space. This paper introduces an evolutionary algorithm using space and localised environments to promote speciation. Surprisingly, a randomly generated “rugged” landscape appears to best support speciation by encouraging crossover between niches, while maintaining locally distinct species.