<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Machine–Learning in Optimization of Expensive Black–Box Functions

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Machine\u2013Learning in Optimization of Expensive Black\u2013Box Functions"}

Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an...

Machine–Learning in Optimization of Expensive Black–Box Functions

Pubblicato online: 04 mag 2017

Pagine: 105 - 118

Ricevuto: 03 mar 2016

Accettato: 11 ott 2016

DOI: https://doi.org/10.1515/amcs-2017-0008

Parole chiave
simulations, metamodels, classifiers, machine learning

© by Yoel Tenne

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Machine–Learning in Optimization of Expensive Black–Box Functions

Pubblicato online: 04 mag 2017

Pagine: 105 - 118

Ricevuto: 03 mar 2016

Accettato: 11 ott 2016

DOI: https://doi.org/10.1515/amcs-2017-0008

Parole chiavesimulations, metamodels, classifiers, machine learning

© by Yoel Tenne

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
simulations, metamodels, classifiers, machine learning