An island based hybrid evolutionary algorithm for optimization.
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.
Citation : Li, 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.
ISBN : 978-3-540-89693-7
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes