Abstract:We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i.e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context. We provide a unified hybrid model using both Monte Carlo tree search (MCTS) and Gaussian processes (GP) that encompasses and generalizes multiple state-of-the-art mixed BO surrogates.Based on the architecture, we propose applying a new dynamic model selection criterion among novel candidate families of covariance kernels, i… Show more
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