Direct Optimization of Experimental Designs, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, pp.2004-4578, 2004. ,
DOI : 10.2514/6.2004-4578
A robust design method using variable transformation and gauss-hermite integration, International Journal for Numerical Methods in Engineering, vol.66, issue.12, pp.1841-1858, 2006. ,
??)-Theory, Evolutionary Computation, vol.15, issue.2, pp.165-188, 1993. ,
DOI : 10.1162/evco.1993.1.2.165
Functions with noise-induced multimodality: a test for evolutionary robust Optimization-properties and performance analysis, IEEE Transactions on Evolutionary Computation, vol.10, issue.5, pp.507-526, 2006. ,
DOI : 10.1109/TEVC.2005.861416
Robust optimization ??? A comprehensive survey, Computer Methods in Applied Mechanics and Engineering, vol.196, issue.33-34, pp.33-343190, 2007. ,
DOI : 10.1016/j.cma.2007.03.003
Gaussian processes for regression and optimisation, 2007. ,
Dependent gaussian processes, Advances in Neural Information Processing Systems 17, pp.217-224, 2005. ,
Adaptive Sampling for Noisy Problems, Proceedings of the Genetic and Evolutionary Computation Conference, pp.947-958, 2004. ,
DOI : 10.1007/978-3-540-24854-5_95
Towards a Better Understanding of Modeling Feasibility Robustness in Engineering Design, Journal of Mechanical Design, vol.122, issue.4, pp.385-394, 1999. ,
DOI : 10.1115/1.1290247
Introduction to Evolutionary Computing (Natural Computing Series), 2008. ,
Optimization using surrogate models and partially converged computational fluid dynamics simulations, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.462, issue.2071, pp.4622177-2204, 2006. ,
DOI : 10.1098/rspa.2006.1679
Global Optimization of Deceptive Functions with Sparse Sampling, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008. ,
DOI : 10.2514/6.2008-5996
Kriging Is Well-Suited to Parallelize Optimization, Computational Intelligence in Expensive Optimization Problems, Springer series in Evolutionary Learning and Optimization, pp.131-162, 2009. ,
DOI : 10.1007/978-3-642-10701-6_6
URL : https://hal.archives-ouvertes.fr/emse-00436126
Gaussian process priors with uncertain inputs -application to multiple-step ahead time series forecasting, Advances in Neural Information Processing Systems, pp.529-536, 2003. ,
Approximate methods for propagation of uncertainty with gaussian process models, 2004. ,
The CMA evolution strategy: a comparing review Advances on estimation of distribution algorithms, pp.75-102, 2006. ,
A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion, IEEE Transactions on Evolutionary Computation, vol.13, issue.1, pp.180-197, 2009. ,
DOI : 10.1109/TEVC.2008.924423
URL : https://hal.archives-ouvertes.fr/inria-00276216
Space and space-time modeling using process convolutions. Quantitative methods for current environmental issues, pp.37-56, 2002. ,
DOI : 10.1007/978-1-4471-0657-9_2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.5356
Sequential kriging optimization using multiple-fidelity evaluations. Structural and Multidisciplinary Optimization, pp.369-382, 2006. ,
DOI : 10.1007/s00158-005-0587-0
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models, Journal of Global Optimization, vol.25, issue.1, pp.441-466, 2006. ,
DOI : 10.1007/s10898-005-2454-3
SNOBFIT -- Stable Noisy Optimization by Branch and Fit, ACM Transactions on Mathematical Software, vol.35, issue.2, pp.1-25, 2008. ,
DOI : 10.1145/1377612.1377613
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.5642
The use of metamodeling techniques for optimization under uncertainty, ASME Design Automation Conference, Paper No. DAC21039, pp.99-116, 2001. ,
DOI : 10.1007/s00158-002-0277-0
Evolutionary Optimization in Uncertain Environments???A Survey, IEEE Transactions on Evolutionary Computation, vol.9, issue.3, pp.303-317, 2005. ,
DOI : 10.1109/TEVC.2005.846356
A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, vol.21, pp.345-383, 2001. ,
Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998. ,
DOI : 10.1023/A:1008306431147
Predicting the output from a complex computer code when fast approximations are available, Biometrika, vol.87, issue.1, pp.1-13, 2000. ,
DOI : 10.1093/biomet/87.1.1
Gaussian processes for global optimization, 3rd International Conference on Learning and Intelligent Optimization, 2009. ,
Formulations for surrogate-based optimization under uncertainty. 9 th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization ,
Bayes???Hermite quadrature, Journal of Statistical Planning and Inference, vol.29, issue.3, pp.245-260, 1991. ,
DOI : 10.1016/0378-3758(91)90002-V
Towards real-time information processing of sensor network data using computationally efficient multi-output gaussian processes, International Conference on Information Processing in Sensor Networks, pp.109-120, 2008. ,
Nonstationary Gaussian Processes for Regression and Spatial Modelling, 2003. ,
Robust Design: An Overview, AIAA Journal, vol.44, issue.1, pp.181-191, 2006. ,
DOI : 10.2514/1.13639
Noisy expected improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity, EngOpt 2010 -2nd International Conference on Engineering Optimization ,
URL : https://hal.archives-ouvertes.fr/hal-00489321
Propagation of uncertainty in bayesian kernel models -application to multiple-step ahead forecasting, International Conference on Acoustics, Speech and Signal Processing, pp.701-704, 2003. ,
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 2005. ,
Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-423, 1989. ,
DOI : 10.1214/ss/1177012413
Optimization under uncertainty: State-of-the-art and opportunities, Computers and Chemical Engineering, vol.28, pp.971-983, 2004. ,
An Empirical Study of the Use of Confidence Levels in RBDO with Monte-Carlo Simulations, Multidisciplinary Design Optimization in Computational Mechanics, 2009. ,
DOI : 10.1002/9781118600153.ch9
URL : https://hal.archives-ouvertes.fr/emse-00436268
Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, 2002. ,
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations, 2003. ,
DOI : 10.1162/153244303765208386
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.979
Stochastic system design and applications to stochastically robust structural control, 2007. ,
Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria, Journal of Global Optimization, vol.10, issue.2, pp.373-389, 2009. ,
DOI : 10.1007/s10898-008-9313-y
URL : https://hal.archives-ouvertes.fr/hal-00205120
An informational approach to the global optimization of expensive-to-evaluate functions, Journal of Global Optimization, vol.10, issue.5, pp.509-534, 2009. ,
DOI : 10.1007/s10898-008-9354-2
URL : https://hal.archives-ouvertes.fr/hal-00354262
A life cycle engineering approach to development of flexible manufacturing systems, IEEE Transactions on Robotics, vol.19, issue.3, pp.465-473, 2003. ,