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Combining the Strengths of the Bayesian Optimization Algorithm and Adaptive Evolution Strategies |
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AbstractThis paper proposes a method that combines competent genetic algorithms working in discrete domains with adaptive evolution strategies working in continuous domains. We use discretization to transform solution between the two domains. The results of our experiments with the Bayesian optimization algorithm as a discrete optimizer and -self- adaptive mutation of evolution strategies as a continuous optimizer combined using k- means clustering suggest that the algorithm scales up well...
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