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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Vydané v:	IEEE transactions on evolutionary computation Ročník 23; číslo 1; s. 44 - 58
Hlavní autori:	Ding, Jinliang, Yang, Cuie, Jin, Yaochu, Chai, Tianyou
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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