Machine–Learning in Optimization of Expensive Black–Box Functions

Author:

Tenne Yoel1

Affiliation:

1. Department of Mechanical and Mechatronic Engineering Ariel University , Ariel 40700 , Israel

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference44 articles.

1. Arlot, S. (2010). A survey of cross-validation procedures for model selection, Statistics Survey4: 40–79.

2. Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J. and Verleysen, M. (2002). Width optimization of the Gaussian Kernels in radial basis function networks, Proceedings of the 10th European Symposium on Artificial Neural Networks, ESANN 2002, Bruges, Belgium, pp. 425–432.

3. Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V. and Trosset, M.W. (1999). A rigorous framework for optimization of expensive functions by surrogates, Structural Optimization17(1): 1–13.

4. Büche, D., Schraudolph, N.N. and Koumoutsakos, P. (2005). Accelerating evolutionary algorithms with Gaussian process fitness function models, IEEE Transactions on Systems, Man, and Cybernetics C35(2): 183–194.

5. Chipperfield, A., Fleming, P., Pohlheim, H. and Fonseca, C. (1994). Genetic Algorithm TOOLBOX for Use with MATLAB, Version 1.2, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield.

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