A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization

In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required...

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Vydáno v:	Scientific reports Ročník 13; číslo 1; s. 13163 - 17
Hlavní autoři:	Yu, Mengjiao, Wang, Zheng, Dai, Rui, Chen, Zhongkui, Ye, Qianlin, Wang, Wanliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 13.08.2023 Nature Publishing Group Nature Portfolio
Témata:	639/166 639/705 Algorithms Approximation Archives & records Dominance Efficiency Genetic algorithms Humanities and Social Sciences Machine learning multidisciplinary Neural networks Objective function Optimization Science Science (multidisciplinary) Support vector machines Training Variables
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
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