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The Annealing Adaptive Search (AAS) algorithm searches the feasible region of an optimization problem by generating candidate solutions from a sequence of Boltzmann distributions. However, the difficulty of sampling from a Boltzmann distribution at each iteration of the algorithm limits its applications to practical problems. To address this difficulty, we propose an approximation of AAS, called Model-based...
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