Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Mathematics of Operations Research
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Computer experiments and global optimization
Computer experiments and global optimization
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Sequential adaptive designs in computer experiments for response surface model fit
Sequential adaptive designs in computer experiments for response surface model fit
An informational approach to the global optimization of expensive-to-evaluate functions
Journal of Global Optimization
Evolutionary algorithms for minimax problems in robust design
IEEE Transactions on Evolutionary Computation
Learning Viewpoint Planning in Active Recognition on a Small Sampling Budget: A Kriging Approach
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
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A new algorithm is proposed to deal with the worst-case optimization of black-box functions evaluated through costly computer simulations. The input variables of these computer experiments are assumed to be of two types. Control variables must be tuned while environmental variables have an undesirable effect, to which the design of the control variables should be robust. The algorithm to be proposed searches for a minimax solution, i.e., values of the control variables that minimize the maximum of the objective function with respect to the environmental variables. The problem is particularly difficult when the control and environmental variables live in continuous spaces. Combining a relaxation procedure with Kriging-based optimization makes it possible to deal with the continuity of the variables and the fact that no analytical expression of the objective function is available in most real-case problems. Numerical experiments are conducted to assess the accuracy and efficiency of the algorithm, both on analytical test functions with known results and on an engineering application.