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Model-based optimization algorithms are effective for solving optimization problems with little structure. The algorithms iteratively find candidate solutions by generating samples from a parameterized probabilistic model on the solution space. In order to better capture the multi-modality of the objective function than the traditional model-based methods which use only a single model, we propose...
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