Illinois Genetic Algorithms Laboratory

Lima, C. F., Pelikan, M., Sastry, K., Butz, M. V., Goldberg, D. E., Lobo, F. G. (2006)
TR No.: 2006021 | Download PDF | Download PS

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.