Z. Bouzarkouna, A. Auger, and D. Ding, Investigating the Local-Meta-Model CMA-ES for Large Population Sizes, Proc. EvoNUM'10, pp.402-411, 2010.
DOI : 10.1007/978-3-642-12239-2_42

URL : https://hal.archives-ouvertes.fr/hal-00450238

S. Finck, N. Hansen, R. Ros, and A. Auger, Real-parameter black-box optimization benchmarking 2010: Presentation of the noisy functions, 2009.

L. Graning, Y. Jin, and B. Sendhoff, Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study, Proc. ESANN'2005, pp.27-29, 2005.

N. Hansen, A. Auger, S. Finck, and R. Ros, Real-parameter black-box optimization benchmarking 2012: Experimental setup

N. Hansen, S. Finck, R. Ros, and A. Auger, Real-parameter black-box optimization benchmarking 2009: Noisy functions definitions, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00369466

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, pp.159-195, 2001.
DOI : 10.1016/0004-3702(95)00124-7

N. Hansen and R. 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

F. Hoffmann and S. Holemann, Controlled Model Assisted Evolution Strategy with Adaptive Preselection, 2006 International Symposium on Evolving Fuzzy Systems, pp.182-187, 2006.
DOI : 10.1109/ISEFS.2006.251155

H. Ingimundardottir and T. Runarsson, Sampling strategies in ordinal regression for surrogate assisted evolutionary optimization, 2011 11th International Conference on Intelligent Systems Design and Applications, p.page To appear, 2011.
DOI : 10.1109/ISDA.2011.6121815

G. A. Jastrebski and D. V. Arnold, Improving Evolution Strategies through Active Covariance Matrix Adaptation, 2006 IEEE International Conference on Evolutionary Computation, pp.2814-2821, 2006.
DOI : 10.1109/CEC.2006.1688662

S. Kern, N. Hansen, and P. Koumoutsakos, Local Meta-models for Optimization Using Evolution Strategies, PPSN IX, pp.939-948, 2006.
DOI : 10.1007/11844297_95

O. Kramer, Covariance matrix self-adaptation and kernel regression -perspectives of evolutionary optimization in kernel machines, Fundam. Inf, vol.98, pp.87-106, 2010.

I. Loshchilov, M. Schoenauer, and M. Sebag, Comparison-Based Optimizers Need Comparison-Based Surrogates, Proc. PPSN XI, pp.364-373, 2010.
DOI : 10.1007/978-3-642-15844-5_37

URL : https://hal.archives-ouvertes.fr/inria-00493921

I. Loshchilov, M. Schoenauer, and M. Sebag, Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12
DOI : 10.1145/2330163.2330210

URL : https://hal.archives-ouvertes.fr/hal-00686570

K. Price, Differential evolution vs. the functions of the second ICEO, Proceedings of the IEEE International Congress on Evolutionary Computation, pp.153-157, 1997.

T. P. Runarsson, Ordinal Regression in Evolutionary Computation, PPSN IX, pp.1048-1057, 2006.
DOI : 10.1007/11844297_106

H. Ulmer, F. Streichert, and A. Zell, Evolution strategies assisted by gaussian processes with improved pre-selection criterion, Proc. CEC'2003, pp.692-699, 2003.