Defining Locality in Genetic Programming to Predict Performance

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@InProceedings{galvan-lopez_etal_ii:cec2010,

• author = "Edgar Galvan-Lopez and James McDermott and Michael O'Neill and Anthony Brabazon",
• title = "Defining Locality in Genetic Programming to Predict Performance",
• booktitle = "2010 IEEE World Congress on Computational Intelligence",
• pages = "1828--1835",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-6910-9",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2010.5586095",
• abstract = "A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotypefitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.",
• notes = "WCCI 2010. Also known as \cite{5586095}",

}

Genetic Programming entries for Edgar Galvan Lopez James McDermott Michael O'Neill Anthony Brabazon

Citations