The Adaptive Search Strategy Selection Based on the Guide of Two Learning Mechanisms for High-Dimensional Expensive Problems-Reference-Cited by-同舟云学术

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The Adaptive Search Strategy Selection Based on the Guide of Two Learning Mechanisms for High-Dimensional Expensive Problems

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS)
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Author:

Gong Yiyun¹,Kang Li²,Qiao Gangzhu³,Guo Dongpeng⁴,Yu Haibo³,Zeng Jianchao³

Affiliation:

1. School of Electrical and Control Engineering, North University of China,Taiyuan,China

2. School of Environment and Safety Engineering, North University of China,Taiyuan,China

3. School of Computer Science and Technology, North University of China,Taiyuan,China

4. School of Environment and Resources, Taiyuan University of Science and Technology,Taiyuan,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10294323/10294356/10294738.pdf?arnumber=10294738

Reference26 articles.

1. Practical Reinforcement Learning: Develop self-evolving;akhtar;intelligent agents with OpenAI Gym Python and Java,2017

2. Abstract Convex Underestimation Assisted Multistage Differential Evolution

3. A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

4. Evolutionary Sampling Agent for Expensive Problems

5. Ensemble of surrogates assisted particle swarm optimization of medium scale expensive problems

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