Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search


We perform theoretical analysis for a previously proposed method of enhancing performance of an evolutionary algorithm with reinforcement learning. The method adaptively chooses between auxiliary objectives in a single-objective evolutionary algorithm using reinforcement learning. We consider the Q-learning algorithm with ε-greedy strategy (ε > 0), using a… (More)
DOI: 10.1109/CEC.2015.7257102

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