EFFICIENCY INCREASING METHOD OF THE EVOLUTIONARY ALGORITHMS BY REINFORCEMENT LEARNING
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A scalar optimization method based on evolutionary algorithms controlling by reinforcement learning is proposed. Reinforcement learning is used to choose the most effective fitness function at each generation of the evolutionary algorithm. Experimental results for a model problem H-IFF are given. Comparison of the developed method with other evolutionary optimization methods is performed. According to experimental results, the proposed method increases the effectiveness of evolutionary algorithms.