Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution

Published:2024-07 Issue:7 Volume:54 Page:4247-4259
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Guo Hongshu¹^ORCID,Ma Yining²^ORCID,Ma Zeyuan¹^ORCID,Chen Jiacheng¹^ORCID,Zhang Xinglin¹^ORCID,Cao Zhiguang³^ORCID,Zhang Jun⁴^ORCID,Gong Yue-Jiao¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

2. Department of Industrial Systems Engineering and Management, College of Design and Engineering, National University of Singapore, Cluny Road, Singapore

3. School of Computing and Information Systems, Singapore Management University, Victoria Street, Singapore

4. Nankai University, Tianjin, China

Funder

National Natural Science Foundation of China

Guangdong Natural Science Funds for Distinguished Young Scholars

Guangdong Regional Joint Funds for Basic and Applied Research

TCL Young Scholars Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221021/10559915/10496708.pdf?arnumber=10496708

Reference46 articles.

1. Evolutionary algorithms and their applications to engineering problems

2. Adaptation in Natural and Artificial Systems

3. Particle swarm optimization;Kennedy

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