This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies ...

This work 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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Full NameComment goes here.Christos Kannas, Researcher Associate at University of Sheffield 2 years agoDan Grigorovici, Software Manager, Analytics & Targeting at Apple Inc. Tagsoptimization4 years agoloox600Tagsbayesianoptimizationalgorithm5 years agoJohann Dréo, Researcher at TRT Tagsestimation of distribution algorithmgraphmetaheuristicbayesevolutionary-computation6 years ago