Automated Configuration of Evolutionary Algorithms via Deep Reinforcement Learning for Constrained Multiobjective Optimization

Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective optimization evolutionary algorithms (CMOEAs). Current learning-assisted CMOEAs are typically crafted by human experts using manually designed techniq...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 55; H. 12; S. 1 - 14
Hauptverfasser:	Ming, Fei, Gong, Wenyin, Xue, Bing, Zhang, Mengjie, Jin, Yaochu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2025
Schlagworte:	Automated algorithm configuration constrained multiobjective optimization Convergence Cybernetics Deep reinforcement learning DRL evolutionary algorithms (EAs) Evolutionary computation Evolutionary dynamics Heuristic algorithms learning to optimize Linear programming Optimization Training Vectors
ISSN:	2168-2267, 2168-2275
Online-Zugang:	Volltext
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