Expensive Multiobjective Optimization by Relation Learning and Prediction

Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due to their costly evaluation. Building cheap surrogate models to replace the expensive real models has been proved to be a practical way to reduce the number of costly evaluations. Supervised learning t...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 26; číslo 5; s. 1157 - 1170
Hlavní autori:	Hao, Hao, Zhou, Aimin, Qian, Hong, Zhang, Hu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Classifiers Computational modeling Data models Evaluation Evolutionary algorithms Expensive optimization Machine learning multiobjective optimization Multiple objective analysis Optimization Pareto optimization Predictive models relation model surrogate-assisted evolutionary algorithm (SAEA) Training Training data
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
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