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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lima, C. F., Pelikan, M., Sastry, K., Butz, M., Goldberg, D. E., & Lobo, F. G. (2006). Substructural neighborhoods for local search in the Bayesian optimization algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4193 LNCS, pp. 232–241). Springer Verlag. https://doi.org/10.1007/11844297_24
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