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New Ways to Calibrate Evolutionary Algorithms

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Advances in Metaheuristics for Hard Optimization

Part of the book series: Natural Computing Series ((NCS))

Abstract

The issue of setting the values of various parameters of an evolutionary algorithm (EA) is crucial for good performance. One way to do it is by controlling EA parameters on-the-fly, which can be done in various ways and for various parameters. We briefly review these options in general and present the findings of a literature search and some statistics about themost popular options. Thereafter, we provide three case studies indicating a high potential for uncommon variants. In particular, we recommend focusing on parameters regulating selection and population size, rather than those concerning crossover and mutation. On the technical side, the case study on adjusting tournament size shows by example that global parameters can also be selfadapted, and that heuristic adaptation and pure self-adaptation can be successfully combined into a hybrid of the two.

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  1. Department of Computer Science, Vrije Universiteit, 1081, Amsterdam, Netherlands

    Gusz Eiben & Martijn C. Schut

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  1. Gusz Eiben
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  1. Laboratory LiSSi, University of Paris 12, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

  2. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

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Eiben, G., Schut, M. (2007). New Ways to Calibrate Evolutionary Algorithms. In: Siarry, P., Michalewicz, Z. (eds) Advances in Metaheuristics for Hard Optimization. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_8

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