Machine-learning in optimization of expensive black-box functions

• Autorzy: Yoel Tenne
• Języki publikacji: EN

Abstrakty

EN

Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process. To handle such scenarios more effectively, this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Several implementations of the proposed approach are described. An extensive set of numerical experiments shows that the proposed approach improves search effectiveness.

Słowa kluczowe

EN

simulations; metamodels; classifiers; machine learning

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2017
• Tom: 27
• Numer: 1
• Strony: 105-118

Daty

wydano

2017

otrzymano

2016-03-03

poprawiono

2016-09-04

zaakceptowano

2016-10-11

Twórcy

autor

Yoel Tenne

• Department of Mechanical and Mechatronic Engineering, Ariel University, Ariel 40700, Israel

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Identyfikatory

DOI

10.1515/amcs-2017-0008