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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Vydané v:	IEEE transactions on cybernetics Ročník 55; číslo 12; s. 1 - 14
Hlavní autori:	Ming, Fei, Gong, Wenyin, Xue, Bing, Zhang, Mengjie, Jin, Yaochu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2025
Predmet:	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
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