Adaptive non-uniform mutation based on statistics for genetic algorithms

dc.cclicence	N/A	en
dc.contributor.author	Yang, Shengxiang	en
dc.date.accessioned	2017-03-21T16:08:55Z
dc.date.available	2017-03-21T16:08:55Z
dc.date.issued	2002
dc.description.abstract	As a meta-heuristic search algorithm based on mechanisms abstracted from population genetics, the genetic algorithm (GA) implicitly maintains the statistics about the search space through the population. This implicit statistics can be explicitly used to enhance GA's performance. In this paper, a statistics-based adaptive non-uniform mutation (SANUM) is proposed. SANUM uses the statistics information of the allele distribution in each locus to adaptively adjust the mutation operation. Our preliminary experiments show that SANUM outperforms traditional bit flip mutation across a representative set set of test problems.	en
dc.funder	N/A	en
dc.identifier.citation	Yang. S. (2002) Adaptive non-uniform mutation based on statistics for genetic algorithms. In Erick Cantu-Paz (editor), Late-Breaking Papers at the 2002 Genetic and Evolutionary Computation Conference, pp. 490-495	en
dc.identifier.uri	http://hdl.handle.net/2086/13810
dc.language.iso	en_US	en
dc.peerreviewed	Yes	en
dc.projectid	N/A	en
dc.publisher	Menlo Park, CA: AAAI Press	en
dc.researchgroup	Centre for Computational Intelligence	en
dc.researchinstitute	Institute of Artificial Intelligence (IAI)	en
dc.title	Adaptive non-uniform mutation based on statistics for genetic algorithms	en
dc.type	Conference	en

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