Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimi...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 23; H. 1; S. 44 - 58
Hauptverfasser:	Ding, Jinliang, Yang, Cuie, Jin, Yaochu, Chai, Tianyou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Convergence Evolutionary algorithms Evolutionary algorithms (EAs) Evolutionary computation evolutionary multitasking optimization expensive optimization Fitness Knowledge management Knowledge transfer Multitasking Optimization Sociology Statistics
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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