Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Journal
Proceedings of GECCO 2007
Pages
601-608
Date Issued
2007
Author(s)
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.
Type
conference paper