Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities

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.