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Paper details
Number 1 - March 2017
Volume 27 - 2017
Machine-learning in optimization of expensive black-box functions
Yoel Tenne
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.
Keywords
simulations, metamodels, classifiers, machine learning