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.

中文翻译：

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进化计算的通用参数控制框架

本研究通过利用强化学习系统的能力来学习进化计算的经验知识，提出了一个通用的多参数控制框架。我们设计了一个反馈评估机制来定义提供给代理人的奖励，他们可以使用它来学习在制定的动作集中选择合适的参数。此外，还提出了一种学习策略，以利用在训练期间获得的参数选择相关知识。选择三种著名的进化计算 (EC) 方法（即粒子群优化、人工蜂群和差异进化）作为基线算法并将其应用于所提出的框架。上述重新设计的算法在 15 个通用基准函数以及 CEC2017 基准上进行了测试。此外，算法的稳健性通过参数敏感性分析得到证明。比较分析的结果表明，这三种改进算法比其最先进的变体表现出更快的整体收敛性和更高的准确性。还证实了我们提出的框架有能​​力提高 EC 方法的性能。