R. Astudillo and P. Frazier, Multi-attribute bayesian optimization under utility uncertainty, Proceedings of the NIPS Workshop on Bayesian Optimization, 2017.

S. K. Au and J. L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation, Probabilistic Engineering Mechanics, vol.16, issue.4, pp.263-277, 2001.

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler, Articulating user preferences in many-objective problems by sampling the weighted hypervolume, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp.555-562, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00431271

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler, Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp.563-570, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00431274

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.22, issue.3, pp.773-793, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00689580

J. Bect, L. Li, and E. Vazquez, Bayesian subset simulation, SIAM/ASA Journal on Uncertainty Quantification, vol.5, issue.1, pp.762-786, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01253706

J. Bect, R. Sueur, A. Gérossier, L. Mongellaz, S. Petit et al., ´ Echantillonnage préférentiel et méta-modèles: méthodes bayésiennes optimale et défensive. In: 47èmes Journées de Statistique de la SFdS-JdS 2015, 2015.

R. Benassi, Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle, 2013.

F. Cérou, P. Del-moral, T. Furon, and A. Guyader, Sequential Monte Carlo for rare event estimation, Statistics and Computing, vol.22, issue.3, pp.795-808, 2012.

D. Chafekar, J. Xuan, and K. Rasheed, Constrained multi-objective optimization using steady state genetic algorithms, Genetic and Evolutionary ComputationGECCO 2003, pp.813-824, 2003.

P. Del-moral, A. Doucet, and A. Jasra, Sequential monte carlo samplers, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.3, pp.411-436, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01593880

M. Emmerich, Single-and multiobjective evolutionary design optimization assisted by Gaussian random field metamodels, 2005.

M. Emmerich, A. H. Deutz, and I. Yevseyeva, On reference point free weighted hypervolume indicators based on desirability functions and their probabilistic interpretation, Procedia Technology, vol.16, pp.532-541, 2014.

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

M. Emmerich and J. W. Klinkenberg, The computation of the expected improvement in dominated hypervolume of Pareto front approximations, 2008.

P. Feliot, A Bayesian approach to constrained multi-objective optimization, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01323031

P. Feliot, J. Bect, and E. Vazquez, A Bayesian approach to constrained singleand multi-objective optimization, Journal of Global Optimization, vol.67, issue.1-2, pp.97-133, 2017.

D. Ginsbourger and R. Le-riche, Towards Gaussian process-based optimization with finite time horizon. In: mODa 9-Advances in Model-Oriented Design and Analysis, pp.89-96, 2010.
URL : https://hal.archives-ouvertes.fr/emse-00680794

E. C. Harrington, The desirability function, Industrial quality control, vol.21, issue.10, pp.494-498, 1965.

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.

J. Knowles and D. Corne, On metrics for comparing nondominated sets, Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC'02, vol.1, pp.711-716, 2002.

H. J. Kushner, A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise, Journal of Fluids Engineering, vol.86, issue.1, pp.97-106, 1964.

M. Laumanns, G. Rudolph, and H. P. Schwefel, Approximating the pareto set: Concepts, diversity issues, and performance assessment, Secretary of the SFB, vol.531, 1999.

J. Mockus, V. Tiesis, and A. Zilinskas, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, vol.2, pp.117-129, 1978.

T. J. Santner, B. J. Williams, and W. Notz, The design and analysis of computer experiments, 2003.

M. Schonlau, W. J. Welch, and D. R. Jones, Global versus local search in constrained optimization of computer models, New Developments and Applications in Experimental Design: Selected Proceedings of a 1997 Joint AMS-IMS-SIAM Summer Conference, vol.34, pp.11-25, 1998.

T. Wagner and H. Trautmann, Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functions, IEEE Transactions on Evolutionary Computation, vol.14, issue.5, pp.688-701, 2010.

C. K. Williams and C. Rasmussen, Gaussian processes for machine learning. the, vol.2, 2006.

E. Zitzler, D. Brockhoff, and L. Thiele, The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. In: Evolutionary multi-criterion optimization, pp.862-876, 2007.

E. Zitzler and L. Thiele, Multiobjective optimization using evolutionary algorithmsa comparative case study. In: Parallel problem solving from nature-PPSN V, pp.292-301, 1998.