Evolutionary algorithms using a neural network like migration scheme

Article type: Research Article

Authors: Villmann, Thomas

Affiliations: Klinik für Psychotherapie, Universität Leipzig, K.-Tauchnitz-Str.25, 04107 Leipzig, Germany. Tel.: +49 341 9718868; Fax: +49 341 2131257; E-mail: villmann@informatik.uni-leipzig.de

Abstract: We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration scheme of which follows a neural network learning like dynamic. It is adapted from the approach of collective learning in self-organizing maps with a more and more separation during time. We succesfully apply this approach to clustering real world data in psychotherapy research and VLSI-design. The advantages of the approach are shown which consist in a reduced communication overhead between the subpopulations preserving a non-vanishing information flow and an improved convergence rate resulting in decreasing computational costs.

DOI: 10.3233/ICA-2002-9102

Journal: Integrated Computer-Aided Engineering, vol. 9, no. 1, pp. 25-35, 2002