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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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: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_24
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DOI: https://doi.org/10.1007/11844297_24
Publisher Name: Springer, Berlin, Heidelberg
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