Proceedings of the 30th conference on Winter simulation
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
SNOBFIT -- Stable Noisy Optimization by Branch and Fit
ACM Transactions on Mathematical Software (TOMS)
Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.