  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.
