Evolutionary algorithms (EA), a class of stochastic search algorithms based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various 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 are actively exploring 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 the integration of reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). Firstly, we introduce reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. We then 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 strategy is divided according to the implemented functions including the solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Subsequently, other attribute settings of RL in RL-EA are discussed. Finally, we analyze potential directions for future research. This paper serves as a comprehensive resource for researchers who are interested in RL-EA as it provides an overview of the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.

翻译：进化算法（EA）是一类基于自然进化原理的随机搜索算法，因其在各类优化问题中的卓越性能而广受赞誉。尽管全球研究者已提出多种多样的EA，但其仍存在收敛速度慢、泛化能力差等局限。为此，众多学者正积极探索对算法结构、算子、搜索模式等方面的改进以提升优化性能。近年来，将强化学习（RL）作为组件整合至EA框架中展现出优越性能。本文对强化学习与进化算法的融合方法（即强化学习辅助进化算法，简称RL-EA）进行了全面综述。首先，我们介绍强化学习与进化算法的基本概念。随后，提出RL-EA的分类体系。进而基于现有文献，探讨RL-EA的集成方法、采用的RL辅助策略及其应用场景。RL辅助策略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。接着，讨论RL-EA中RL的其他属性设置。最后，分析未来研究的潜在方向。本文为关注RL-EA的研究者提供了全面参考，阐述了当前技术前沿并凸显相关挑战。通过本综述，读者可快速掌握RL-EA的核心思想以开发高效算法，从而推动这一新兴领域的进一步发展。