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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5866

Title: An island based hybrid evolutionary algorithm for optimization
Authors: Li, C
Yang, S
Publication Date: 2008
Publisher: Springer Verlag
Citation: 7th International Conference on Simulated Evolution and Learning, 5361: 180 - 189, 2008
Abstract: Evolutionary 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.
Description: This is a post-print version of the article - Copyright @ 2008 Springer-Verlag
Sponsorship: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1.
URI: http://www.springerlink.com/content/a072826053062l51/
http://bura.brunel.ac.uk/handle/2438/5866
DOI: http://dx.doi.org/10.1007/978-3-540-89694-4_19
ISSN: 0302-9743
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Publications
Computer Science

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