Abstract
In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.
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© 1991 Springer-Verlag Berlin Heidelberg
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De Jong, K.A., Spears, W.M. (1991). An analysis of the interacting roles of population size and crossover in genetic algorithms. In: Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029729
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DOI: https://doi.org/10.1007/BFb0029729
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Print ISBN: 978-3-540-54148-6
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