Cooperative Co-evolution Inspired Operators for Classical GP Schemes

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Aichour:2007:NICSO,

• author = "Malek Aichour and Evelyne Lutton",
• title = "Cooperative Co-evolution Inspired Operators for Classical GP Schemes",
• booktitle = "Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)",
• year = "2007",
• pages = "169--178",
• editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta",
• volume = "129",
• series = "Studies in Computational Intelligence",
• address = "Acireale, Italy",
• month = "8-10 " # nov,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-78986-4",
• DOI = "doi:10.1007/978-3-540-78987-1_16",
• abstract = "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 optimisation 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",
• notes = "http://www.dmi.unict.it/nicso2007/ http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf",

}

Genetic Programming entries for Malek Aichour Evelyne Lutton

Citations