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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Cooperative Co-evolution Inspired Operators for Classical GP Schemes
verfasst von : Malek Aichour, Evelyne Lutton
Erschienen in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)
Verlag: Springer Berlin Heidelberg
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This work is a first step toward the design of a cooperative-coevolution GP for symbolic regression, which first output is a selective mutation operator for classical GP. Cooperative co-evolution techniques rely on the imitation of cooperative capabilities of natural populations and have been successfully applied in various domains to solve very complex optimization problems. It has been proved on several applications that the use of two fitness measures (local and global) within an evolving population allow to design more efficient optimization schemes. We currently investigate the use of a two-level fitness measurement for the design of operators, and present in this paper a selective mutation operator. Experimental analysis on a symbolic regression problem give evidence of the efficiency of this operator in comparison to classical subtree mutation.