Unsupervised clustering and multi-optima evolutionary search

This paper pursues a course of investigation of an approach to combine Evolutionary Computation and Data Mining for the location and computation of multiple local and global optima of an objective function. To accomplish this task we exploit the spatial concentration of the population members around the optima of the objective function. Such concentration regions are determined by applying clustering algorithms on the actual positions of the members of the population. Subsequently, the evolutionary search is confined in the interior of the regions discovered. To enable the simultaneous discovery of more than one global and local optima, we propose the use of clustering algorithms that also provide intuitive approximations for the number of clusters. Furthermore, the proposed scheme has often the potential of accelerating the convergence speed of the Evolutionary Algorithm, without the need for extra function evaluations.