TY - JOUR
T1 - Machine-learning in optimization of expensive black-box functions
AU - Tenne, Yoel
N1 - Publisher Copyright:
© 2017 by Yoel Tenne.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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.
AB - 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.
KW - classifiers
KW - machine learning
KW - metamodels
KW - simulations
UR - http://www.scopus.com/inward/record.url?scp=85017384672&partnerID=8YFLogxK
U2 - 10.1515/amcs-2017-0008
DO - 10.1515/amcs-2017-0008
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AN - SCOPUS:85017384672
SN - 1641-876X
VL - 27
SP - 105
EP - 118
JO - International Journal of Applied Mathematics and Computer Science
JF - International Journal of Applied Mathematics and Computer Science
IS - 1
ER -