A Mixed Strategy of Combining Evolutionary Algorithms with Multigrid Methods

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dc.contributor.author Kang, Lishan
dc.contributor.author He, Jun
dc.date.accessioned 2009-05-28T07:37:17Z
dc.date.available 2009-05-28T07:37:17Z
dc.date.issued 2009-05-28
dc.identifier.citation Kang , L & He , J 2009 , ' A Mixed Strategy of Combining Evolutionary Algorithms with Multigrid Methods ' International Journal of Computer Mathematics , pp. 837-849 . en
dc.identifier.other PURE: 115434
dc.identifier.other dspace: 2160/2414
dc.identifier.uri http://hdl.handle.net/2160/2414
dc.description J. He, and L. Kang. A Mixed Strategy of Combining Evolutionary Algorithms with Multigrid Methods. International Journal of Computer Mathematics, 86(5):837-849, 2009. en
dc.description.abstract Multigrid methods have been proven to be an efficient approach in accelerating the convergence rate of numerical algorithms for solving partial differential equations. This paper investigates whether multigrid methods are helpful to accelerate the convergence rate of evolutionary algorithms for solving global optimization problems. A novel multigrid evolutionary algorithm is proposed and its convergence is proven. The algorithm is tested on a set of 13 well-known benchmark functions. Experiment results demonstrate that multigrid methods can accelerate the convergence rate of evolutionary algorithms and improve their performance. en
dc.format.extent 13 en
dc.language.iso eng
dc.relation.ispartof International Journal of Computer Mathematics en
dc.title A Mixed Strategy of Combining Evolutionary Algorithms with Multigrid Methods en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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