An expensive multi-objective evolutionary algorithm based on grid and relation learning

In many real-world applications, there is often a need to optimize multiple objectives, which are frequently and simultaneously conflicting. The evaluation process consumes computational resources or funds, making it difficult to provide adequate function evaluations for converging evolutionary algo...

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Veröffentlicht in:	Applied soft computing Jg. 186; S. 114135
Hauptverfasser:	Cheng, Yan, Wang, Jiaqi, Yu, Gongcheng, Yao, Yuxiao, Chen, Yanyin, Li, Guowei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2026
Schlagworte:	Evolutionary algorithms Expensive multi-objective optimization Grid Relation learning Surrogate-assisted optimization Evolutionary algorithms Surrogate-assisted optimization Relation learning Expensive multi-objective optimization Grid
ISSN:	1568-4946
Online-Zugang:	Volltext
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