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dc.contributor.authorLi, Changheen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-06-13T13:06:39Z
dc.date.available2013-06-13T13:06:39Z
dc.date.issued2008
dc.identifier.citationLi, C. and Yang, S. (2008) An island based hybrid evolutionary algorithm for optimization. In: Simulated evolution and learning: Proceedings of the 7th International Conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008. Berlin: Springer-Verlag, pp. 180-189.en
dc.identifier.isbn978-3-540-89693-7
dc.identifier.urihttp://hdl.handle.net/2086/8740
dc.description.abstractEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.en
dc.language.isoenen
dc.publisherSpringer-Verlag.en
dc.relation.ispartofseriesLecture notes in computer science;Vol. 5361
dc.titleAn island based hybrid evolutionary algorithm for optimization.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-540-89694-4_19
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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