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. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews 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方法，因其在各类实际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，但仍有收敛速度慢、泛化能力差等局限性。为此，众多学者积极探索算法结构、算子、搜索模式等方面的改进，以提升其优化性能。近年来，将强化学习（RL）作为组件集成到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进行了全面综述，称为强化学习辅助进化算法（RL-EA）。我们首先概述强化学习与进化算法的核心概念，继而提出RL-EA的分类体系。在此基础上，依据现有文献讨论RL-EA的集成方法、所采用的RL辅助策略及其应用。RL辅助流程根据实现功能划分为解生成、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。最后，我们分析未来潜在的研究方向。本综述为关注RL-EA的研究人员提供丰富资料，系统梳理当前技术前沿并揭示相关挑战。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。