Learnable Evolutionary Search Across Heterogeneous Problems via Kernelized Autoencoding

The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent years. It has been demonstrated that the knowledge embedded in the past search experience can greatly speed up the evolutionary process if properly harnes...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 25; číslo 3; s. 567 - 581
Hlavní autori:	Zhou, Lei, Feng, Liang, Gupta, Abhishek, Ong, Yew-Soon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Construction Crashworthiness Domains Evolutionary algorithms Evolutionary optimization Genetic algorithms Hilbert space Impact strength Kernel kernelization Knowledge management Knowledge transfer Machine learning Mapping Multiple objective analysis Noise reduction nonlinear Nonlinearity Optimization Search problems Search process Sociology
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
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