An Estimation of Distribution Algorithm Based on Maximum Entropy

Alden Wright¹, Riccardo Poli², Chris Stephens³, W.B. Langdon⁴, and Sandeep Pulavarty¹

¹Computer Science, University of Montana, Missoula, MT, 59812, USA

²Department of Computer Science, University of Essex, Colchester, UK

³Instituto de Ciencias Nucleares, UNAM, Mexico DF 04510

⁴Computer Science, University College, London, Gower Street, London, UK

Abstract. Estimation of distribution algorithms (EDA) are similar to genetic algorithms except that they replace crossover and mutation with sampling from an estimated probability distribution. We develop a framework for estimation of distribution algorithms based on the principle of maximum entropy and the conservation of schema frequencies. An algorithm of this type gives better performance than a standard genetic algorithm (GA) on a number of standard test problems involving deception and epistasis (i.e. Trap and NK).

LNCS 3103, p. 343 ff.

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