An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 26; číslo 4; s. 631 - 645
Hlavní autori:	Lin, Qiuzhen, Wu, Xunfeng, Ma, Lijia, Li, Jianqiang, Gong, Maoguo, Coello, Carlos A. Coello
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Computational modeling Ensemble surrogate Evolutionary algorithms Evolutionary computation Genetic algorithms Model accuracy model management multiobjective optimization Multiple objective analysis Optimization Predictive models Sorting algorithms Space exploration Subspaces Training Uncertainty
ISSN:	1089-778X, 1941-0026
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