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In this paper we demonstrate how ideas from thermodynamics as well as evolutionary biology can be combined to form powerful optimization strategies. These strategies however introduce new parameters to an already parameter dependent optimization task. By testing the introduced strategies with numerous optimization parameter settings for three model problems we develop a semi-automatic parameter adaption which allows an easy control of the free search parameters for potential users.
Published Online: 2009-09-25
Published in Print: 2002-01
