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:	arXiv.org
Hlavní autori:	Liu, Songbai, Lin, Qiuzhen, Li, Jianqiang, Kay Chen Tan
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 27.02.2023
Predmet:	Discriminators Evolutionary algorithms Evolutionary computation Genetic algorithms Machine learning Mopping Multiple objective analysis Optimization Problem solving Scaling up Taxonomy
ISSN:	2331-8422
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