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General parameter control framework for evolutionary computation
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DOI：10.1002/int.23049.png)
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Abstract
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This study proposes a general multiple parameter control framework by leveraging the ability of a reinforcement learning system to learn empirical knowledge for evolutionary computation. We design a feedback evaluation mechanism to define the rewards offered to agents, using which they can learn to choose appropriate parameters in formulated action sets. Moreover, a learning strategy is proposed to utilize the parameter selection-related knowledge that is gained during training episodes. Three famous evolutionary computation (EC) methods (i.e., particle swarm optimization, artificial bee colony, and differential evolution) are selected as the baseline algorithms and applied to the proposed framework. The aforementioned redesigned algorithms are tested on 15 common benchmark functions, as well as the CEC2017 benchmarks. In addition, the robustness of the algorithms is demonstrated through parameter sensitivity analysis. The results of the comparative analysis reveal that the three improved algorithms exhibit a faster overall convergence and higher accuracy than their state-of-the-art variants. It is also confirmed that our proposed framework has the capability to improve the performance of EC approach.
Keywords:
automatic parameter tuning
evolutionary computation
parameter control
reinforcement learning
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