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2017 | 27 | 1 | 105-118
Tytuł artykułu

Machine-learning in optimization of expensive black-box functions

Autorzy
Treść / Zawartość
Warianty tytułu
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
Rocznik
Tom
27
Numer
1
Strony
105-118
Opis fizyczny
Daty
wydano
2017
otrzymano
2016-03-03
poprawiono
2016-09-04
zaakceptowano
2016-10-11
Twórcy
autor
  • Department of Mechanical and Mechatronic Engineering, Ariel University, Ariel 40700, Israel
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv27i1p105bwm
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