Machine–Learning in Optimization of Expensive Black–Box Functions

International Journal of Applied Mathematics and Computer Science. 2017;27(1):105-118 DOI 10.1515/amcs-2017-0008


Journal Homepage

Journal Title: International Journal of Applied Mathematics and Computer Science

ISSN: 2083-8492 (Online)

Publisher: Sciendo

Society/Institution: University of Zielona Góra & Lubuskie Scientific Society

LCC Subject Category: Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Country of publisher: Poland

Language of fulltext: English

Full-text formats available: PDF



Tenne Yoel (Department of Mechanical and Mechatronic Engineering Ariel University, Ariel40700, Israel)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 53 weeks


Abstract | Full Text

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.