DONG Hong-Bin1, YANG Bao-Di1, LIU Jia-Yuan2, HOU Wei1
1.College of Computer Science and Technology,Harbin Engineering University,Harbin 150001 2.College of Information and Communication Engineering,Harbin Engineering University, Harbin 150001
Abstract A co-evolutionary algorithm for clustering is proposed. Firstly, the number of centers of clusters can be decided automatically with an improved mask code manner. The population is divided into two subpopulations which are constituted of the same size of individuals. The genetic algorithm is used in one subpopulation which is good at global search optimum ability, and the differential evolution algorithm is used in the other which has good local search ability to cluster. In the evolution process, different migration policies are utilized to exchange good individuals found by the two evolutionary algorithms between the twosubpopulations, which can balance the global and local search ability of the proposed algorithm. The experimental results show that the proposed method is effective through testing the number of the centers of clusters, performance and execution time on several datasets.
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2012, Vol. 25 
