A Fine-Grained View of Phenotypes and Locality in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{McDermott:2011:GPTP,

• author = "James McDermott and Edgar Galvan-Lopez and Michael O'Neill",
• title = "A Fine-Grained View of Phenotypes and Locality in Genetic Programming",
• booktitle = "Genetic Programming Theory and Practice IX",
• year = "2011",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore",
• series = "Genetic and Evolutionary Computation",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• publisher = "Springer",
• chapter = "4",
• pages = "57--76",
• keywords = "genetic algorithms, genetic programming, Evolutionary computation, fitness landscape, problem difficulty, phenotype, locality, artificial ant, Boolean problems",
• isbn13 = "978-1-4614-1769-9",
• DOI = "doi:10.1007/978-1-4614-1770-5_4",
• abstract = "The locality of the mapping from genotype to phenotype is an important issue in the study of landscapes and problem difficulty in evolutionary computation. In tree-structured Genetic Programming (GP), the locality approach is not generally applied because no explicit genotype-phenotype mapping exists, in contrast to some other GP encodings. we define GP phenotypes in terms of semantics or behaviour. For a given problem, a model of one or more phenotypes and mappings between them may be appropriate e.g. g -> p_0, where g is the genotype, p_i are distinct types of phenotypes and f is fitness. Thus, the behaviour of each component mapping can be studied separately. The locality of the genotype-phenotype mapping can also be decomposed into the effects of the encoding and those of the operator's genotypic step-size. Two standard benchmark problem classes, Boolean and artificial ant, are studied in a principled way using this fine-grained view of locality. The method of studying locality with phenotypes seems useful in the case of the artificial ant, but Boolean problems provide a counter-example.",
• notes = "part of \cite{Riolo:2011:GPTP}",
• affiliation = "Evolutionary Design and Optimization, CSAIL, MIT, Cambridge, USA",

}

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

Citations