A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems-Reference-Cited by-同舟云学术

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A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems

Published:2024-04 Issue:2 Volume:8 Page:1894-1909
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Luo Jianping¹^ORCID,Dong Yongfei¹,Liu Qiqi²^ORCID,Zhu Zexuan³^ORCID,Cao Wenming¹^ORCID,Tan Kay Chen⁴^ORCID,Jin Yaochu²^ORCID

Affiliation:

1. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

2. School of Engineering, Westlake University, Hangzhou, China

3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

4. Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

Funder

National Natural Science Foundation of China

Scientific Research and Development Foundations of Shenzhen

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10480102/10433214.pdf?arnumber=10433214

Reference52 articles.

1. A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

2. NAS-Unet: Neural Architecture Search for Medical Image Segmentation

3. Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization

4. Multiobjective Multitasking Optimization With Subspace Distribution Alignment and Decision Variable Transfer

5. Angle-Based Multi-Objective Evolutionary Algorithm Based On Pruning-Power Indicator for Game Map Generation

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Solving Large-Scale Sparse Multi-Objective Optimization Problems by Automated Decision Variable Clustering;2024

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