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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning

Crossref DOI link: https://doi.org/10.1007/s00521-024-09903-8

Published Online: 2024-05-16

Published Print: 2024-09

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Gong, Yiyun

Yu, Haibo https://orcid.org/0000-0003-2638-6485
Kang, Li

Sun, Chaoli

Zeng, Jianchao
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (62106237)

Joint Funds of the National Natural Science Foundation of China (U21A20524)

Shanxi Province Science Foundation for Youths (202203021222057)
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Text and Data Mining valid from 2024-05-16

Version of Record valid from 2024-05-16
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Article History

Received: 27 November 2023

Accepted: 23 April 2024

First Online: 16 May 2024

Declarations

:

: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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