Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research.

翻译：进化算法（EA）是一类基于自然进化原理的随机搜索方法，因其在各类实际优化问题中的卓越表现而广受赞誉。尽管全球研究者已提出多种多样的EA，但仍存在收敛速度慢、泛化能力差等局限。因此，众多学者积极探索对算法结构、算子、搜索模式等的改进以提升优化性能。近年来，将强化学习（RL）作为组件集成到EA框架中已展现出卓越性能。本文对将强化学习集成至进化算法（即强化学习辅助进化算法，RL-EA）进行了全面综述。我们首先概述了强化学习与进化算法的概念框架，随后提出RL-EA的分类体系，进而依据现有文献论述RL-EA的集成方法、所采用的RL辅助策略及其应用。RL辅助过程根据实现功能分为解生成、可学习目标函数、算法/算子/子种群选择、参数自适应及其他策略。此外，还讨论了RL在RL-EA中的不同属性设置。在RL-EA应用章节中，我们展示了RL-EA在多个基准测试及一系列公开数据集上的优异表现，以促进快速对比研究。最后，我们分析了未来研究的潜在方向。