Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima

In this paper a new clustering operator for evolutionary algorithms is proposed. The operator incorporates the unsupervised k-windows clustering algorithm, utilizing already computed pieces of information regarding the search space in an attempt to discover regions containing groups of individuals located close to different minimizers. Consequently, the search is confined inside these regions and a large number of global and local minima of the objective function can be efficiently computed. Extensive experiments shown that the proposed approach is effective and reliable, and greatly accelerates the convergence speed of the considered algorithms.