Estimation of density algorithms (EDAs) have been developed for optimization of discrete, continuous, or mixed discrete and continuous simulation-based design problems. EDAs construct a probability distribution on the set of highest performing designs and sample the distribution for the next generation of solutions. In previous work, the authors have demonstrated how classifier-guided sampling can also be used for discrete variable, discontinuous design space exploration. In this paper we develop the rationale for using classifier-guided sampling as a simple step beyond EDAs that not only improves the characterization of the highest performing designs but also identifies the poorly performing designs and exploits that information for faster convergence to optimal solutions. The resulting method is novel in its use of Bayesian priors to model the inherent uncertainty in a probability distribution that is based on a limited number of samples from the design space. The new classifier-guided method is applied to several example problems and convergence rates are presented that compare favorably to random search and a basic EDA implementation.
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