Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and oft...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on cybernetics Jg. 51; H. 8; S. 3925 - 3937
Hauptverfasser:	Li, Jian-Yu, Zhan, Zhi-Hui, Wang, Hua, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Buildings Data models Data-driven evolutionary algorithm (DDEA) Design optimization ensemble surrogates Evolutionary algorithms Evolutionary computation genetic algorithm (GA) Genetic algorithms Iron Neural networks Optimization Perturbation Perturbation methods Radial basis function
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!