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2006

MEDAL Report 2006007
Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm
Claudio F. Lima, Martin Pelikan, Kumara Sastry, Martin Butz, David E. Goldberg, and Fernando G. Lobo  (2006)

Abstract. This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.


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Missouri Estimation of Distribution Algorithms Laboratory
Department of Mathematics and Computer Science
University of Missouri-St. Louis, St. Louis, MO

                   

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