Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence

This paper analyzes convergence properties of the Bayesian optimization algorithm (BOA). It settles the BOA into the framework of problem decomposition used frequently in order to model and understand the behavior of simple genetic algorithms. The growth of the population size and the number of generations until convergence with respect to the size of a problem are theoretically-analyzed. The theoretical results are supported by a number of experiments.

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