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Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities by Song, Yanjie; Wu, Yutong; Guo, Yangyang; Yan, Ran; Suganthan, P. N.; Zhang, Yue; Pedrycz, Witold; Das, Swagatam; Mallipeddi, Rammohan; Feng, Oladayo Solomon Ajani. Qiang is a scholarly article available to read on EtoBox.

What is Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities about?

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 divide

Author

Song, Yanjie; Wu, Yutong; Guo, Yangyang; Yan, Ran; Suganthan, P. N.; Zhang, Yue; Pedrycz, Witold; Das, Swagatam; Mallipeddi, Rammohan; Feng, Oladayo Solomon Ajani. Qiang

Published

2023

Language

EN