Population sizing for entropy-based model building in discrete estimation of distribution algorithms

Authors:
Tian-Li Yu;Kumara Sastry;David E. Goldberg;Martin Pelikan
Affiliations:
National Taiwan University;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Missouri-St. Louis
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

This paper proposes a population-sizing model for entropy-based model building in discrete estimation of distribution algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of selection pressure on population sizing is also preliminarily incorporated. The proposed model indicates that the population size required for building an accurate model scales as Θ(m log m), where m is the number of substructures of the given problem and is proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.