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Machine-learning in optimization of expensive black-box functions

100%

Tenne Y.

International Journal of Applied Mathematics and Computer Science

2017

tom 27

nr 1

105-118

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.

Classifiers for doubly multivariate data

75%

Krzyśko M., Skorzybut M., Wołyński W.

Discussiones Mathematicae Probability and Statistics

2011

tom 31

nr 1-2

5-27

This paper proposes new classifiers under the assumption of multivariate normality for multivariate repeated measures data (doubly multivariate data) with Kronecker product covariance structures. These classifiers are especially useful when the number of observations is not large enough to estimate the covariance matrices, and thus the traditional classifiers fail. The quality of these new classifiers is examined on some real data. Computational schemes for maximum likelihood estimates of required class parameters, and the likelihood ratio test relating to the structure of the covariance matrices, are also given.

Ograniczanie wyników

1 Discussiones Mathematicae Probability and Statistics

1 International Journal of Applied Mathematics and Computer Science

1 Krzyśko M.

1 Skorzybut M.

1 Tenne Y.

1 Wołyński W.

1 2017

1 2011

Wyniki wyszukiwania

Machine-learning in optimization of expensive black-box functions

Classifiers for doubly multivariate data