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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Vydáno v:	Applied soft computing Ročník 186; s. 114135
Hlavní autoři:	Cheng, Yan, Wang, Jiaqi, Yu, Gongcheng, Yao, Yuxiao, Chen, Yanyin, Li, Guowei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.01.2026
Témata:	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
On-line přístup:	Získat plný text
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