Learning to Search Promising Regions by a Monte-Carlo Tree Model-Reference-Cited by-同舟云学术

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Learning to Search Promising Regions by a Monte-Carlo Tree Model

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Xia Hai¹,Li Changhe¹,Zeng Sanyou²,Tan Qingshan¹,Wang Junchen¹,Yang Shengxiang³

Affiliation:

1. School of Automation, China University of Geosciences,Wuhan,China,430074

2. School of Mechanical Engineering and Electronic Information, China University of Geosciences,Wuhan,China,430074

3. School of Computer Science and Informatics, De Montfort University,Leicester,United Kingdom,LEI 9BH

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China University of Geosciences (Wuhan)

111 project

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870216/9870201/09870281.pdf?arnumber=9870281

Reference29 articles.

1. Fitness sharing and niching methods revisited

2. The influence of migration sizes and intervals on island models

3. PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation

4. Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

5. Beating the ‘world champion’ evolutionary algorithm via REVAC tuning;smit;Proceedings of the 2010 IEEE Congress on Evolutionary Computation,0

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1. Learning to search promising regions by space partitioning for evolutionary methods;Swarm and Evolutionary Computation;2024-12

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