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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Expert systems with applications Jg. 265; S. 126050
Hauptverfasser:	Liu, Haitao, Wang, Chia-Hung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.03.2025
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!