Uniform Subtree Mutation

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{vanbelle:2002:EuroGP,

• title = "Uniform Subtree Mutation",
• author = "Terry {Van Belle} and David H. Ackley",
• editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
• booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002",
• volume = "2278",
• series = "LNCS",
• pages = "152--161",
• publisher = "Springer-Verlag",
• address = "Kinsale, Ireland",
• publisher_address = "Berlin",
• month = "3-5 " # apr,
• year = "2002",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-43378-3",
• URL = "http://www.cs.unm.edu/~treport/tr/02-02/uniform_mutation.ps.gz",
• DOI = "doi:10.1007/3-540-45984-7_15",
• abstract = "Genetic programming methods often suffer from `code bloat,' in which evolving solution trees rapidly become unmanageably large. To provide a measure of sensitivity to tree size in a natural way, we introduce a simple uniform subtree mutation (USM) operator that provides an approximately constant probability of mutation per tree node, rather than per tree. To help model circumstances where tree size cannot be ignored, we introduce a new notion of computational effort called size effort. Initial empirical tests show that genetic programming using only uniform subtree mutation reduces evolved tree sizes dramatically, compared to crossover, but does impact solution quality somewhat. In some cases, however, using using a combination of USM and crossover yielded both smaller trees and superior performance, as measured both by size effort and traditional metrics.",
• notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",

}

Genetic Programming entries for Terry Van Belle David H Ackley

Citations