Evolution strategy and hierarchical clustering

In most real world optimization problems, one tries to determine the global among some or even numerous local solutions within the feasible region of parameters. Nevertheless, it could be worthwhile to investigate some of the local solutions as well. A most desirable behavior would be that the optimization strategy behaves globally and yields additional information about local minima detected on the way to the global solution. In this paper, a clustering algorithm has been implemented into an extended higher order evolution strategy in order to achieve these goals. Multimodal two-dimensional test problems, namely, Rastrigin´s function and the 4-parameter die mold press benchmark problem (Takahashi, 1996), are solved using this approach