A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 27; číslo 6; s. 1941 - 1961
Hlavní autori:	Liu, Songbai, Lin, Qiuzhen, Li, Jianqiang, Tan, Kay Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Evolution (biology) Evolutionary algorithms Evolutionary computation Generators Genetic algorithms Learnable evolutionary algorithms Machine learning machine learning (ML) Multiple objective analysis Optimization scalable multiobjective optimization Scaling Search problems Sociology Statistics Taxonomy
ISSN:	1089-778X, 1941-0026
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