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...