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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 4; H. 3; S. 369 - 384
Hauptverfasser:	Chen, Yongliang, Zhong, Jinghui, Feng, Liang, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!