An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization

Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select...

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Vydané v:	IEEE transactions on emerging topics in computational intelligence Ročník 4; číslo 3; s. 369 - 384
Hlavní autori:	Chen, Yongliang, Zhong, Jinghui, Feng, Liang, Zhang, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	adaptive strategy Archives & records Artificial intelligence Crossovers dynamic control Evolutionary algorithm Evolutionary algorithms Evolutionary computation Genetic algorithms Knowledge management Knowledge transfer many-task optimization multi-task optimization Multiple objective analysis Optimization Similarity Sociology Statistics Task analysis
ISSN:	2471-285X, 2471-285X
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