Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum. The exploration-exploitation tradeoff (EET) plays a crucial role in EC, which, however, has traditionally been governed by manually designed rules. In this paper, we propose a deep reinforcement learning-based framework that autonomously configures and adapts the EET throughout the EC search process. The framework allows different individuals of the population to selectively attend to the global and local exemplars based on the current search state, maximizing the cooperative search outcome. Our proposed framework is characterized by its simplicity, effectiveness, and generalizability, with the potential to enhance numerous existing EC algorithms. To validate its capabilities, we apply our framework to several representative EC algorithms and conduct extensive experiments on the augmented CEC2021 benchmark. The results demonstrate significant improvements in the performance of the backbone algorithms, as well as favorable generalization across diverse problem classes, dimensions, and population sizes. Additionally, we provide an in-depth analysis of the EET issue by interpreting the learned behaviors of EC.
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Tsinghua University（清华大学）；Tongji University（同济大学）；University of California, Davis（加州大学戴维斯分校）；Arizona State University（亚利桑那州立大学）；Ohio State University（俄亥俄州立大学）；Zhongguancun Laboratory（中关村实验室）
Institute of Systems Science, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统科学研究所）；Nanjing University（南京大学）；Central South University（中南大学）；City University of Hong Kong（香港城市大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
