Summary: 1
Evolution Strategy and Hierarchical Clustering
Ch. Magele+, O. Aichholzer#, F.Aurenhammer#, B. Brandst˜atter+,
H. Krasser#, M. M˜uhlmann+, W. Renhart+
+ Institute for Fundamentals and Theory in Electrical Engineering
# Institute for Theoretical Computer Science
Graz University of Technology, Kopernikusgasse 24, A­8010 Graz, Austria
e­mail: magele@igte.tu­graz.ac.at
Abstract--- Multi­objective optimization problems, in gen­
eral, exhibit several local optima besides a global one. A
desirable feature of any optimization strategy would there­
fore be to supply the user with as many information as pos­
sible about local optima on the way to the global solution.
In this paper a hierarchical clustering algorithm is imple­
mented into a higher order Evolution Srategy is applied to
achieve these goals.
I. Introduction
Stochastic methods have been widely accepted for the
solution of real world optimization problems. A substan­
tial di#erence to deterministic methods is, that stochastic

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