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Niching Evolution Strategy with Cluster Algorithms O. Aichholzer, F. Aurenhammer, B. Brandstatter, T. Ebner, H. Krasser, Ch. Magele
 

Summary: 1
Niching Evolution Strategy with Cluster Algorithms
O. Aichholzer, F. Aurenhammer, B. Brandstšatter, T. Ebner, H. Krasser, Ch. Magele
Abstract--- In most real world optimization problems one
tries to determine the global among some or even numer­
ous local solutions within the feasible region of parameters.
One 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 min­
ima 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.
Keywords---Stochastic Optimization, Evolution Strategies,
Clustering Methods
I. Introduction
Stochastic optimization methods can be successfully ap­
plied to problems, where there is no or very little knowl­
edge about the behaviour of the objective function, about
the presence of local minima or the distribution of feasi­

  

Source: Aurenhammer, Franz - Institute for Theoretical Computer Science, Technische Universität Graz
Krasser, Hannes - Institute for Theoretical Computer Science, Technische Universität Graz
Technische Universität Graz, Institute for Software Technology

 

Collections: Computer Technologies and Information Sciences