Empirical Investigation of Multiparent Recombination Operators in Evolution Strategies

Author:

Eiben Agoston E.1,Bäck Thomas2

Affiliation:

1. Department of Mathematics and Computer Science Leiden University Niels Bohrweg 1 NL-2333 CA, Leiden The Netherlands

2. Informatik Centrum Dortmund Center for Applied Systems Analysis Joseph-von-Fraunhofer-Strasse 20 D-44227 Dortmund, Germany and Department of Mathematics and Computer Science Leiden University Niels Bohrweg 1 NL-2333 CA, Leiden The Netherlands

Abstract

An extension of evolution strategies to multiparent recombination involving a variable number ϱ of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differing in their modality and separability and the regular/irregular arrangement of their local optima. Multiparent diagonal crossover and uniform scanning crossover and a multiparent version of intermediary recombination are considered in the experiments. The performance of the algorithm is observed to depend on the particular combination of recombination operator and objective function. In most of the cases a significant increase in performance is observed as the number of parents increases. However, there might also be no significant impact of recombination at all, and for one of the unimodal objective functions, the performance is observed to deteriorate over the course of evolution for certain choices of the recombination operator and the number of parents. Additional experiments with a skewed initialization of the population clarify that intermediary recombination does not cause a search bias toward the origin of the coordinate system in the case of domains of variables that are symmetric around zero.

Publisher

MIT Press - Journals

Subject

Computational Mathematics

Reference11 articles.

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2. Back, T., Fogel, D. & Michalewicz, Z. (Eds.) (1997). Handbook of evolutionary computation. New York: Oxford University Press.

3. Back, T, & Michalewicz, 2. (1997). Test landscapes. In T. Back, D. Fogel, & Z. Michalewicz (Eds.), Handbook of evolutionary computation (pp. B2.7: 14-B2.7:20). New York: Oxford University Press.

4. Back, T, & Schwefel, H.P. (1993). An overview ofevolutionary algorithms for parameter optimization. Evolutionary Computation, 1 (I), 1-2 3.

5. Belew, K. & Booker, L. (Eds.) (1991). Proceedings of the Foziith Oiteirzntional Conference on Genetic :llgorithms. San Illateo, Ch: Morgan Kaufmann.

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