Evolutionary Programming Using a Mixed Strategy Adapting to Local Fitness Landscape

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dc.contributor.author Jun, He
dc.contributor.author Liang, Shen
dc.date.accessioned 2010-09-14T13:44:21Z
dc.date.available 2010-09-14T13:44:21Z
dc.date.issued 2010-09-14
dc.identifier.citation Jun , H & Liang , S 2010 , ' Evolutionary Programming Using a Mixed Strategy Adapting to Local Fitness Landscape ' pp. 92-97 . en
dc.identifier.other PURE: 160096
dc.identifier.other dspace: 2160/5690
dc.identifier.uri http://hdl.handle.net/2160/5690
dc.description L. Shen and J. He. Evolutionary Programming Using a Mixed Strategy Adapting to Local Fitness Landscape. Proceedings of the 2009 UK Workshop on Computational Intelligence, pp. 92-97, 2009. en
dc.description.abstract Different mutation operators have been developed in evolutionary programming. However, each operator may only be efficient in certain specific fitness landscapes only. In order to combine the advantages of different operators, the mixed strategy has been proposed and achieved some success. Currently, the design of mixed strategy is mainly based on the performance of applying each operator (i.e. a strategy which produce a higher fitness will receive a better reward), but little is relevant directly to the information of local fitness landscape. In this paper a new mixed strategy is proposed which adapts to local fitness landscape. A measure is given to describe the feature of local fitness landscape. Based on this measure, a mixed strategy is designed for adapting local fitness landscape. Experiment of results on several tests show that the algorithm is efficient. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Evolutionary Programming Using a Mixed Strategy Adapting to Local Fitness Landscape en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Aberystwyth University en
dc.description.status Non peer reviewed en


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