A Mixed Strategy for Evolutionary Programming Based on Local Fitness Landscape

H...............H

Show simple item record

dc.contributor.author Jun, He
dc.contributor.author Liang, Shen
dc.date.accessioned 2010-09-17T08:13:10Z
dc.date.available 2010-09-17T08:13:10Z
dc.date.issued 2010-09-17
dc.identifier.citation Jun , H & Liang , S 2010 , ' A Mixed Strategy for Evolutionary Programming Based on Local Fitness Landscape ' pp. 350 . en
dc.identifier.other PURE: 160181
dc.identifier.other dspace: 2160/5694
dc.identifier.uri http://hdl.handle.net/2160/5694
dc.description L. Shen and J. He, ¿A mixed strategy for evolutionary programming based on local fitness landscape,¿ in Proceedings of 2010 IEEE World Congress on Computational Intelligence (CEC'2010). pp. 350¿357, 2010. en
dc.description.abstract The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the initial scale of the whole population can be very large. This may lead to the conventional EP taking too long to reach convergence. To combat this problem, EP has been modified in various ways. In particular, modifications of the mutation operator may significantly improve the performance of EP. However, operators are only efficient within certain fitness landscapes. The mixed strategies have therefore been proposed in order to combine the advantages of different operators. The design of a mixed strategy is currently based on the performance of applying individual operators. Little is directly relevant to the information of local fitness landscapes. This paper presents a modified mixed strategy, which automatically adapts to local fitness landscapes, and implements a training procedure to choose an optimal mixed strategy for a given typical fitness landscape. The proposed algorithm is tested on a suite of 23 benchmark functions, demonstrating the advantages of this work in that it is less likely to be stuck in local optima and has a faster and better convergence. en
dc.format.extent 350 en
dc.language.iso eng
dc.relation.ispartof en
dc.title A Mixed Strategy for Evolutionary Programming Based on 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


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

My Account