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.

V. Picheny, D. Ginsbourger, and Y. Richet, Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00489321

. Inria,

S. Toscano-palmerin and P. Frazier, Bayesian optimization with expensive integrands, 2018.

H. Ishibuchi, N. Tsukamoto, and Y. Nojima, Evolutionary many-objective optimization: A short review, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.2419-2426, 2008.

C. K. Kay-chen-tan and . Goh, Handling Uncertainties in Evolutionary Multi-Objective Optimization, pp.262-292, 2008.

C. Barrico and C. H. Antunes, Robustness analysis in multi-objective optimization using a degree of robustness concept, 2006 IEEE International Conference on Evolutionary Computation, pp.1887-1892, 2006.

M. Li, S. Azarm, and V. Aute, A multi-objective genetic algorithm for robust design optimization, Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO '05, pp.771-778, 2005.

K. Deb and H. Gupta, Introducing robustness in multi-objective optimization, Evolutionary Computation, vol.14, issue.4, pp.463-494, 2006.

H. Eskandari, C. D. Geiger, and R. Bird, Handling uncertainty in evolutionary multiobjective optimization: Spga, IEEE Congress on Evolutionary Computation, pp.4130-4137, 2007.

E. J. Hughes, Evolutionary Multi-objective Ranking with Uncertainty and Noise, pp.329-343, 2001.

J. E. Fieldsend and R. M. Everson, Multi-objective optimisation in the presence of uncertainty, IEEE Congress on Evolutionary Computation, vol.1, pp.243-250, 2005.

J. Teich, Pareto-Front Exploration with Uncertain Objectives, pp.314-328, 2001.

L. J. David, D. W. Alexander, J. M. Bulger, H. E. Calvin, R. L. Romeijn et al., Approximate implementations of pure random search in the presence of noise, Journal of Global Optimization, vol.31, issue.4, pp.601-612, 2005.

J. Walter, G. Gutjahr, . Ch, and . Pflug, Simulated annealing for noisy cost functions, Journal of Global Optimization, vol.8, issue.1, pp.1-13, 1996.

A. ?ilinskas, On similarities between two models of global optimization: statistical models and radial basis functions, Journal of Global Optimization, vol.48, issue.1, pp.173-182, 2010.

D. W. Gong, N. Na-qin, and X. Sun, Evolutionary algorithms for multi-objective optimization problems with interval parameters, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp.411-420, 2010.

P. Limbourg and D. E. Aponte, An optimization algorithm for imprecise multi-objective problem functions, IEEE Congress on Evolutionary Computation, vol.1, pp.459-466, 2005.

G. L. Soares, F. G. Guimaraes, C. A. Maia, J. A. Vasconcelos, and L. Jaulin, Interval robust multi-objective evolutionary algorithm, IEEE Congress on Evolutionary Computation, pp.1637-1643, 2009.

X. Du, Unified uncertainty analysis by the first order reliability method, Journal of Mechanical Design, vol.130, issue.9, pp.91401-091401, 2008.

P. Limbourg, Multi-objective Optimization of Problems with Epistemic Uncertainty, pp.413-427, 2005.

M. Mlakar, T. Tusar, and B. Filipic, Comparing solutions under uncertainty in multiobjective optimization, Mathematical Problems in Engineering, pp.1-10, 2014.

F. Fusi and P. M. Congedo, An adaptive strategy on the error of the objective functions for uncertainty-based derivative-free optimization, Journal of Computational Physics, vol.309, pp.241-266, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01378411

V. Torzcon, W. Michael, and . Trosset, Using approximations to accelerate engineering design optimization, 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization: A Collection of Technical Papers, pp.738-748, 1998.

M. Yaochu-jin, B. Olhofer, and . Sendhoff, A framework for evolutionary optimization with approximate fitness functions, IEEE Transactions on Evolutionary Computation, vol.6, issue.5, pp.481-494, 2002.

Y. Jin, Surrogate-assisted evolutionary computation: Recent advances and future challenges, Swarm and Evolutionary Computation, vol.1, issue.2, pp.61-70, 2011.

M. Emmerich, A. Giotis, and M. Özdemir, Thomas Bäck, and Kyriakos Giannakoglou. Metamodel-Assisted Evolution Strategies, pp.361-370, 2002.

M. T. Emmerich, K. C. Giannakoglou, and B. Naujoks, Single-and multiobjective evolutionary optimization assisted by gaussian random field metamodels, IEEE Transactions on Evolutionary Computation, vol.10, issue.4, pp.421-439, 2006.

D. Buche, N. N. Schraudolph, and P. Koumoutsakos, Accelerating evolutionary algorithms with gaussian process fitness function models, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.35, issue.2, pp.183-194, 2005.