- Authors:
- Michael W. Trosset,
- Virginia Torczon
Engineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented.
Cited By
- Loshchilov I, Schoenauer M and Sebag M Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es) Proceedings of the 15th annual conference on Genetic and evolutionary computation, (439-446)
- Huyer W and Neumaier A (2008). SNOBFIT -- Stable Noisy Optimization by Branch and Fit, ACM Transactions on Mathematical Software (TOMS), 35:2, (1-25), Online publication date: 25-Jul-2008.
- Barton R Simulation metamodels Proceedings of the 30th conference on Winter simulation, (167-176)
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