A. [. Arnold, N. Auger, Y. Hansen, and . Ollivier, Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. ArXiv e-prints, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00601503

P. [. Agarwal and . Niyogi, Generalization Bounds for Ranking Algorithms via Algorithmic Stability, J. of Machine Learning Research, vol.10, pp.441-474, 2009.

N. [. Buche, P. Schraudolph, and . 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.
DOI : 10.1109/TSMCC.2004.841917

H. [. Bailey, X. S. Yozo, B. Li, and . Thompson, ARPREC: An Arbitrary Precision Computation Package, 2002.
DOI : 10.2172/817634

Z. [. Chu and . Ghahramani, Preference learning with Gaussian processes, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.137-144, 2005.
DOI : 10.1145/1102351.1102369

G. [. Clemençon, N. Lugosi, and . Vayatis, Ranking and Empirical Minimization of U-statistics. The Annals of Statistics, pp.844-874, 2008.

A. [. Hansen, S. Auger, R. Finck, and . Ros, Real-Parameter Black-Box Optimization Benchmarking 2012: Experimental Setup

]. N. Han13 and . Hansen, References to CMA- ES Applications. Website, 2013.

]. N. Har-+-10, A. Hansen, R. Auger, S. Ros, P. Finck et al., Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, GECCO Companion, pp.1689-1696, 2010.

]. N. Hfra09a, S. Hansen, R. Finck, A. Ros, and . Auger, Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions, 2009.

]. N. Hfra09b, S. Hansen, R. Finck, A. Ros, and . Auger, Real-Parameter Black-Box Optimization Benchmarking 2009: Noisy Functions Definitions, 2009.

M. [. Hennig and . Kiefel, Quasi-Newton Methods: A New Direction, 2012.

S. [. Hansen, P. Müller, and . Koumoutsakos, Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Evolutionary Computation, vol.11, issue.1, pp.1-18, 2003.
DOI : 10.1162/106365601750190398

R. [. Hansen and . Ros, Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1673-1680, 2010.
DOI : 10.1145/1830761.1830788

URL : https://hal.archives-ouvertes.fr/hal-00545728

]. Y. Jin11 and . Jin, Surrogate-Assisted Evolutionary Computation: Recent Advances and Future Challenges. Swarm and Evolutionary Computation, pp.61-70, 2011.

]. T. Joa05 and . Joachims, A Support Vector Method for Multivariate Performance Measures, Proc. 22nd ICML, pp.377-384, 2005.

R. Donald, M. Jones, W. J. Schonlau, and . Welch, Efficient Global Optimization of Expensive Black-Box Functions, J. of Global Optimization, vol.13, issue.4, pp.455-492, 1998.

H. [. List and . Simon, SVMoptimization and steepest-descent line search, Proceedings of the 22nd COLT, 2009.

M. [. Loshchilov and M. Schoenauer, Se- bag. Self-Adaptive Surrogate-Assisted CMA-ES, Proc. GECCO, pp.321-328, 2012.

D. [. Moré and . Sorensen, Computing a Trust Region Step, SIAM Journal on Scientific and Statistical Computing, vol.4, issue.3, pp.553-572, 1983.
DOI : 10.1137/0904038

]. M. Pow87 and . Powell, Updating Conjugate Directions by the BFGS Formula, Mathematical Programming, vol.38, issue.1, pp.29-46, 1987.

D. [. Rubinstein and . Kroese, The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization , Monte-Carlo Simulation and Machine Learning, 2004.

]. D. Sha70 and . Shanno, Conditioning of Quasi- Newton Methods for Function Minimiza- tion, Mathematics of Computation, vol.24, issue.111, pp.647-656, 1970.

F. [. Ulmer, A. Streichert, and . Zell, Evolution Strategies assisted by Gaussian Processes with Improved Pre-Selection Criterion, IEEE Congress on Evolutionary Computation, pp.692-699, 2003.