Deep reinforcement learning for dynamic algorithm selection: A proof-of-principle study on differential evolution

• Main Authors: GUO, Hongshu, MA, Yining, MA, Zeyuan, CHEN, Jiacheng, ZHANG, Xinglin, CAO, Zhiguang, ZHANG, Jun, GONG, Yue-Jiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Algorithm selection; deep reinforcement learning; meta-black-box optimization; black-box optimization; differential evolution; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9327; https://ink.library.smu.edu.sg/context/sis_research/article/10327/viewcontent/2403.02131v3.pdf
• Institution: Singapore Management University

Evolutionary algorithms, such as differential evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algorithm selection or configuration. This article aims to address the limitation by leveraging the complementary strengths of a group of algorithms and dynamically scheduling them throughout the optimization progress for specific problems. We propose a deep reinforcement learning-based dynamic algorithm selection framework to accomplish this task. Our approach models the dynamic algorithm selection a Markov decision process, training an agent in a policy gradient manner to select the most suitable algorithm according to the features observed during the optimization process. To empower the agent with the necessary information, our framework incorporates a thoughtful design of landscape and algorithmic features. Meanwhile, we employ a sophisticated deep neural network model to infer the optimal action, ensuring informed algorithm selections. Additionally, an algorithm context restoration mechanism is embedded to facilitate smooth switching among different algorithms. These mechanisms together enable our framework to seamlessly select and switch algorithms in a dynamic online fashion. Notably, the proposed framework is simple and generic, offering potential improvements across a broad spectrum of evolutionary algorithms. As a proof-of-principle study, we apply this framework to a group of differential evolution algorithms. The experimental results showcase the remarkable effectiveness of the proposed framework, not only enhancing the overall optimization performance but also demonstrating favorable generalization ability across different problem classes.