D. Bertsimas, D. B. Brown, and C. Caramanis, Theory and Applications of Robust Optimization, SIAM Review, vol.53, issue.3, pp.464-501, 2011.
DOI : 10.1137/080734510

A. Ben-tal, L. Ghaoui, and A. Nemirovski, Robust Optimization, 2009.
DOI : 10.1515/9781400831050

C. Zang, M. I. Friswell, and J. E. Mottershead, A review of robust optimal design and its application in dynamics, Computers & Structures, vol.83, issue.4-5, pp.315-326, 2005.
DOI : 10.1016/j.compstruc.2004.10.007

H. G. 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

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, 2000.
DOI : 10.1115/1.1290247

R. Jin, X. Du, and W. Chen, The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, pp.99-116, 2003.

J. Janusevskis and R. Le-riche, Simultaneous kriging-based estimation and optimization of mean response, Journal of Global Optimization, vol.10, issue.4, pp.313-336, 2013.
DOI : 10.1007/s10898-011-9836-5

URL : https://hal.archives-ouvertes.fr/emse-00674460

B. J. Williams, T. J. Santner, and W. I. Notz, Sequential design of computer experiments to minimize integrated response functions, Statistica Sinica, vol.10, issue.4, pp.1133-1152, 2000.

B. Rustem and M. Howe, Algorithms for Worst-Case Design and Applications to Risk Management, 2002.
DOI : 10.1515/9781400825110

J. Marzat, E. Walter, and H. Piet-lahanier, Worst-case global optimization of black-box functions through Kriging and relaxation, Journal of Global Optimization, vol.14, issue.6, pp.707-727, 2013.
DOI : 10.1007/s10898-012-9899-y

URL : https://hal.archives-ouvertes.fr/hal-00682475

A. M. Cramer, S. D. Sudhoff, and E. L. Zivi, Evolutionary Algorithms for Minimax Problems in Robust Design, IEEE Transactions on Evolutionary Computation, vol.13, issue.2, pp.444-453, 2009.
DOI : 10.1109/TEVC.2008.2004422

R. I. Lung and D. Dumitrescu, A new evolutionary approach to minimax problems, 2011 IEEE Congress of Evolutionary Computation (CEC), pp.1902-1905, 2011.
DOI : 10.1109/CEC.2011.5949847

D. Du and P. M. Pardalos, Minimax and Applications, 1995.
DOI : 10.1007/978-1-4613-3557-3

P. Parpas and B. Rustem, An Algorithm for the Global Optimization of a Class of??Continuous Minimax Problems, Journal of Optimization Theory and Applications, vol.15, issue.3, pp.461-473, 2009.
DOI : 10.1007/s10957-008-9473-4

K. Shimizu and E. Aiyoshi, Necessary conditions for min-max problems and algorithms by a relaxation procedure, IEEE Transactions on Automatic Control, vol.25, issue.1, pp.62-66, 1980.
DOI : 10.1109/TAC.1980.1102226

D. R. Jones, M. J. 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

J. Mockus, Bayesian Approach to Global Optimization: Theory and Applications, 1989.

H. J. Kushner, A versatile stochastic model of a function of unknown and time varying form, Journal of Mathematical Analysis and Applications, vol.5, issue.1, pp.150-167, 1962.
DOI : 10.1016/0022-247X(62)90011-2

D. R. Jones, A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, vol.21, issue.4, pp.345-383, 2001.
DOI : 10.1023/A:1012771025575

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

T. J. Santner, B. J. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

M. Schonlau, Computer Experiments and Global Optimization, 1997.

D. C. Montgomery, Design and Analysis of Experiments, 2008.

D. R. Jones, C. D. Perttunen, and B. E. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications, vol.20, issue.1, pp.157-181, 1993.
DOI : 10.1007/BF00941892

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

Y. D. Sergeyev and D. E. Kvasov, Global Search Based on Efficient Diagonal Partitions and a Set of Lipschitz Constants, SIAM Journal on Optimization, vol.16, issue.3, pp.910-937, 2006.
DOI : 10.1137/040621132

E. Vazquez and J. Bect, Convergence properties of the expected improvement algorithm with fixed mean and covariance functions, Journal of Statistical Planning and Inference, vol.140, issue.11, pp.3088-3095, 2010.
DOI : 10.1016/j.jspi.2010.04.018

URL : https://hal.archives-ouvertes.fr/hal-00217562

A. D. Bull, Convergence rates of efficient global optimization algorithms, Journal of Machine Learning Research, vol.12, pp.2879-2904, 2011.

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

J. Villemonteix, E. Vazquez, 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

J. Marzat, E. Walter, F. Damongeot, and H. Piet-lahanier, Robust automatic tuning of diagnosis methods via an efficient use of costly simulations, Proceedings of the 16th IFAC Symposium on System Identification, pp.398-403, 2012.
DOI : 10.3182/20120711-3-BE-2027.00053

URL : https://hal.archives-ouvertes.fr/hal-00680764

J. Marzat, E. Walter, and H. Piet-lahanier, A new strategy for worst-case design from costly numerical simulations, 2013 American Control Conference, pp.3991-3996, 2013.
DOI : 10.1109/ACC.2013.6580450

URL : https://hal.archives-ouvertes.fr/hal-00837731

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.34, issue.4, pp.773-793, 2012.
DOI : 10.1007/s11222-011-9241-4

URL : https://hal.archives-ouvertes.fr/hal-00689580

C. Chevalier and D. Ginsbourger, Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection, Learning and Intelligent Optimization, pp.59-69, 2013.
DOI : 10.1007/978-3-642-44973-4_7

URL : https://hal.archives-ouvertes.fr/hal-00732512

A. Zhou and Q. Zhang, A surrogate-assisted evolutionary algorithm for minimax optimization, IEEE Congress on Evolutionary Computation, pp.1-7, 2010.
DOI : 10.1109/CEC.2010.5586122

S. E. Randall, Optimum Vibration Absorbers for Linear Damped Systems, Journal of Mechanical Design, vol.103, issue.4, pp.908-913, 1978.
DOI : 10.1115/1.3255005

E. Pennestri, AN APPLICATION OF CHEBYSHEV'S MIN???MAX CRITERION TO THE OPTIMAL DESIGN OF A DAMPED DYNAMIC VIBRATION ABSORBER, Journal of Sound and Vibration, vol.217, issue.4, pp.757-765, 1998.
DOI : 10.1006/jsvi.1998.1805

B. Brown and T. Singh, Minimax design of vibration absorbers for linear damped systems, Journal of Sound and Vibration, vol.330, issue.11, pp.2437-2448, 2011.
DOI : 10.1016/j.jsv.2010.12.002

F. A. Viana, G. I. Kotinda, D. A. Rade, and V. Steffen-jr, Tuning dynamic vibration absorbers by using ant colony optimization, Computers & Structures, vol.86, issue.13-14, pp.13-141539, 2008.
DOI : 10.1016/j.compstruc.2007.05.009

V. Dubourg, B. Sudret, and J. Bourinet, Reliability-based design optimization using Kriging surrogates and subset simulation. Structural and Multidisciplinary Optimization, pp.673-690, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00587311

C. Chevalier, Fast uncertainty reduction strategies relying on Gaussian process models, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00879082

J. Quiñonero-candela and C. E. Rasmussen, A unifying view of sparse approximate gaussian process regression, The Journal of Machine Learning Research, vol.6, pp.1939-1959, 2005.