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

Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive proce...

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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 7; H. 4; S. 1051 - 1064
Hauptverfasser:	Tian, Ye, Li, Xiaopeng, Ma, Haiping, Zhang, Xingyi, Tan, Kay Chen, Jin, Yaochu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Convergence Deep learning Evolutionary algorithm Evolutionary algorithms Machine learning multi-objective optimization Multiple objective analysis Neural networks operator selection Operators Optimization Particle swarm optimization Reinforcement learning Search process Sociology Statistics
ISSN:	2471-285X, 2471-285X
Online-Zugang:	Volltext
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