SEAMS: A surrogate-assisted evolutionary algorithm with metric-based dynamic strategy for expensive multi-objective optimization

In real-world scenarios where resources for evaluating expensive optimization problems are limited and the reliability of trained models is hard to assess, the quality of the non-dominated front formed by algorithms tends to be low. This paper proposes a metric-based surrogate-assisted evolutionary...

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Vydané v:	Expert systems with applications Ročník 265; s. 126050
Hlavní autori:	Liu, Haitao, Wang, Chia-Hung
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.03.2025
Predmet:	Classification Evolutionary algorithms Expensive multi-objective optimization Front modeling Model management Surrogate-assisted optimization Front modeling Evolutionary algorithms Surrogate-assisted optimization Expensive multi-objective optimization Classification Model management
ISSN:	0957-4174
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