Niching evolution strategy with cluster algorithms

O. Aichholzer, F. Aurenhammer, B. Brandtstaetter, T. Ebner, H. Krasser, and C. Magele

Abstract:

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. On the other hand, it could be worth to investigate some of the local solutions as well. Therefore, a most desirable behaviour would be, if 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 Higher Order Evolution Strategy in order to achieve these goals.



Reference: O. Aichholzer, F. Aurenhammer, B. Brandtstaetter, T. Ebner, H. Krasser, and C. Magele. Niching evolution strategy with cluster algorithms. In $9^{th}$ Biennial IEEE Conf. Electromagnetic Field Computations, Milwaukee, Wisconsin, USA, 2000.