Probabilistic Selection Approaches in Decomposition-Based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization

In offline data-driven multiobjective optimization, no new data are available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogate...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 26; číslo 5; s. 1182 - 1191
Hlavní autori:	Mazumdar, Atanu, Chugh, Tinkle, Hakanen, Jussi, Miettinen, Kaisa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation Decomposition Evolutionary algorithms Gaussian processes kernel density estimation Kriging Linear programming metamodeling Multiple objective analysis Optimization Pareto optimality Probabilistic logic Probability density function Sociology Statistics surrogate Uncertainty
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
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