Presents a hybrid evolutionary search method based on clusters (HESC). The method is specifically designed to enhance the search efficiency while alleviating the problem of premature convergence inherent in standard evolutionary search methods (SES). It involves the simultaneous evolution of a main species and an additional fast mutating species. A hybrid search method which includes a local parallel single agent search and a global multiagent evolutionary search is carried out for the main species. Effective utilization of the search history is achieved with the clustering and training of a fuzzy ART neural network (ART NN) during the search. The advantages of HESC include: (1) guaranteed population diversity at each generation; (2) effective integration of local search for the exploitation of important regions and the global search for the exploration of the entire space; and (3) fast exploration ability of the fast mutating species and migration from the additional species to the main species. Those advantages have been confirmed with experiments in which hard optimization problems were successfully solved with HESC.