Combining The Strengths Of Bayesian Optimization Algorithm And Adaptive Evolution Strategies

This 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 discrete optimizer and σ-self-adaptive mutation of evolution strategies as a continuous optimizer combined using k-means clustering suggest that the algorithm scales up well on all tested problems. The proposed method can be used to fill the gap between other optimization methods working in continuous and discrete domains and allow their hybridization.

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