J. Auzins, Direct Optimization of Experimental Designs, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, pp.2004-4578, 2004.
DOI : 10.2514/6.2004-4578

H. Beiqing and D. Xiaoping, 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.

H. Beyer, ??)-Theory, Evolutionary Computation, vol.15, issue.2, pp.165-188, 1993.
DOI : 10.1162/evco.1993.1.2.165

H. Beyer and B. Sendhoff, 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

H. Beyer and B. Sendhoff, 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

P. Boyle, Gaussian processes for regression and optimisation, 2007.

P. Boyle and M. Frean, Dependent gaussian processes, Advances in Neural Information Processing Systems 17, pp.217-224, 2005.

E. Cantú-paz, 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

X. Du and W. Chen, 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

A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing (Natural Computing Series), 2008.

A. I. Forrester, N. W. Bressloff, and A. J. Keane, 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

A. I. Forrester and D. R. Jones, Global Optimization of Deceptive Functions with Sparse Sampling, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008.
DOI : 10.2514/6.2008-5996

D. Ginsbourger, R. L. Riche, and L. Carraro, 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

A. Girard, C. E. Rasmussen, J. Quiñonero-candela, and R. Murray-smith, Gaussian process priors with uncertain inputs -application to multiple-step ahead time series forecasting, Advances in Neural Information Processing Systems, pp.529-536, 2003.

A. Girard, Approximate methods for propagation of uncertainty with gaussian process models, 2004.

N. Hansen, The CMA evolution strategy: a comparing review Advances on estimation of distribution algorithms, pp.75-102, 2006.

N. Hansen, S. P. André, L. Niederberger, P. Guzzella, and . Koumoutsakos, 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

D. Higdon, 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

D. Huang, T. Allen, W. Notz, and R. Miller, Sequential kriging optimization using multiple-fidelity evaluations. Structural and Multidisciplinary Optimization, pp.369-382, 2006.
DOI : 10.1007/s00158-005-0587-0

D. Huang, T. T. Allen, W. I. Notz, and N. Zeng, 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

W. Huyer and A. Neumaier, 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

R. Jin, X. Du, and W. Chen, 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

Y. Jin and J. Branke, 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

R. Donald and . Jones, A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, vol.21, pp.345-383, 2001.

D. R. Jones, M. Schonlau, and W. J. Welch, 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

M. C. Kennedy and A. O. Hagan, 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

R. Garnett, M. A. Osborne, and S. J. Roberts, Gaussian processes for global optimization, 3rd International Conference on Learning and Intelligent Optimization, 2009.

S. F. Wojkiewicz, T. Trucano, M. S. Eldred, and A. A. Giunta, Formulations for surrogate-based optimization under uncertainty. 9 th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

A. O. Hagan, 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

A. Michael, A. Osborne, S. Rogers, . Ramchurn, J. Stephen et al., 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.

C. J. Paciorek, Nonstationary Gaussian Processes for Regression and Spatial Modelling, 2003.

G. Park, T. Lee, H. L. Kwon, and K. Hwang, Robust Design: An Overview, AIAA Journal, vol.44, issue.1, pp.181-191, 2006.
DOI : 10.2514/1.13639

V. Picheny, D. Ginsbourger, and Y. Richet, 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

J. Quiñonero-candela, A. Girard, J. Larsen, and C. E. Rasmussen, 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.

E. Carl, C. K. Rasmussen, and . Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 2005.

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-423, 1989.
DOI : 10.1214/ss/1177012413

V. Nikolaos and . Sahinidis, Optimization under uncertainty: State-of-the-art and opportunities, Computers and Chemical Engineering, vol.28, pp.971-983, 2004.

D. Salazar, R. L. Riche, and X. Bay, 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

J. Michael and . Sasena, Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, 2002.

M. Seeger, 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

A. Taflanidis, Stochastic system design and applications to stochastically robust structural control, 2007.

J. Villemonteix, E. Vazquez, M. Sidorkiewicz, and E. Walter, 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

J. Villemonteix, E. Vázquez, and E. Walter, 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

P. Yan and M. Zhou, A life cycle engineering approach to development of flexible manufacturing systems, IEEE Transactions on Robotics, vol.19, issue.3, pp.465-473, 2003.