D. Arnold, Noisy Optimization With Evolution Strategies, 2002.
DOI : 10.1007/978-1-4615-1105-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.383.9099

D. Arnold, Weighted multirecombination evolution strategies, Theoretical Computer Science, vol.361, issue.1, pp.18-37, 2006.
DOI : 10.1016/j.tcs.2006.04.003

D. Arnold and H. Beyer, A comparison of evolution strategies with other direct search methods in the presence of noise, Computational Optimization and Applications, vol.24, issue.1, pp.135-159, 2003.
DOI : 10.1023/A:1021810301763

A. Auger, Convergence results for the <mml:math altimg="si1.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>??</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>-SA-ES using the theory of <mml:math altimg="si2.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mi>??</mml:mi></mml:math>-irreducible Markov chains, Theoretical Computer Science, vol.334, issue.1-3, pp.35-69, 2005.
DOI : 10.1016/j.tcs.2004.11.017

A. Auger and N. Hansen, Performance Evaluation of an Advanced Local Search Evolutionary Algorithm, 2005 IEEE Congress on Evolutionary Computation, pp.1777-1784, 2005.
DOI : 10.1109/CEC.2005.1554903

A. Auger and N. Hansen, A Restart CMA Evolution Strategy With Increasing Population Size, 2005 IEEE Congress on Evolutionary Computation, pp.1769-1776, 2005.
DOI : 10.1109/CEC.2005.1554902

N. Beume and G. Rudolph, Faster S-metric calculation by considering dominated hypervolume as Klee's measure problem, IASTED International Conference on Computational Intelligence, pp.231-236, 2006.

N. Beume, B. Naujoks, and M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, vol.181, issue.3, pp.1653-1669, 2007.
DOI : 10.1016/j.ejor.2006.08.008

H. Beyer, The Theory of Evolution Strategies Evolution strategies, p.1965, 2001.

H. Beyer and H. Schwefel, Evolution strategies, Scholarpedia, vol.2, issue.8, pp.3-52, 2002.
DOI : 10.4249/scholarpedia.1965

K. Deb, K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2001.
DOI : 10.1109/4235.996017

M. Emmerich, N. Beume, and B. Naujoks, An EMO Algorithm Using the Hypervolume Measure as Selection Criterion, Third International Conference on Evolutionary Multi-Criterion Optimization, pp.62-76, 2005.
DOI : 10.1007/978-3-540-31880-4_5

D. Fogel, Evolutionary Computation, IEEE Congress on Evolutionary Computation, pp.1157-1163, 1995.
DOI : 10.1002/9781119214403.ch10

F. Friedrichs and C. Igel, Evolutionary tuning of multiple SVM parameters, Neurocomputing, vol.64, issue.C, pp.107-117, 2005.
DOI : 10.1016/j.neucom.2004.11.022

T. Glasmachers and C. Igel, Uncertainty Handling in Model Selection for Support Vector Machines, ) Parallel Problem Solving from Nature (PPSN X), pp.185-194, 2008.
DOI : 10.1007/978-3-540-87700-4_19

F. Gomez, J. Schmidhuber, and R. Miikkulainen, Efficient Non-linear Control Through Neuroevolution, Proceedings of the European Conference on Machine Learning, pp.654-662, 2006.
DOI : 10.1007/11871842_64

M. Grewal and A. Andrews, Kalman Filtering: Theory and Practice Updating the inverse of a matrix, SIAM Review, vol.31, issue.2, pp.221-239, 1989.

N. Hansen, Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie Eine Untersuchung zur entstochastisierten, koordinatensystemunabhängigen Adaptation der Mutationsverteilung, 1998.

N. Hansen, The CMA evolution strategy: a comparing review (eds) Towards a new evolutionary computation Advances on estimation of distribution algorithms, pp.75-102, 2006.

N. Hansen, S. Kern, X. Yao, E. Burke, J. Lozano et al., Evaluating the CMA Evolution Strategy on Multimodal Test Functions, Parallel Problem Solving from Nature (PPSN VIII), pp.282-291, 2004.
DOI : 10.1007/978-3-540-30217-9_29

N. Hansen and A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, Proceedings of IEEE International Conference on Evolutionary Computation, pp.312-317, 1996.
DOI : 10.1109/ICEC.1996.542381

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, A. Ostermeier, and A. Gawelczyk, On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation, Proceedings of the Sixth International Conference on Genetic Algorithms, pp.57-64, 1995.

N. Hansen, S. Müller, and P. 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

N. Hansen, S. Niederberger, L. Guzzella, and P. Koumoutsakos, A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion, IEEE Transactions on Evolutionary Computation, vol.13, issue.1, 2008.
DOI : 10.1109/TEVC.2008.924423
URL : https://hal.archives-ouvertes.fr/inria-00276216

V. Heidrich-meisner and C. Igel, Evolution Strategies for Direct Policy Search, ) Parallel Problem Solving from Nature (PPSN X), pp.428-437, 2008.
DOI : 10.1007/978-3-540-87700-4_43

V. Heidrich-meisner and C. Igel, Similarities and differences between policy gradient methods and evolution strategies, 16th European Symposium on Artificial Neural Networks Evere, Belgien: d-side publications, pp.427-432, 2008.

V. Heidrich-meisner and C. Igel, Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem, European Workshop on Reinforcement Learning, pp.136-150, 2008.
DOI : 10.1007/978-3-540-89722-4_11

C. Igel, R. Sarker, R. Reynolds, H. Abbass, K. Tan et al., Neuroevolution for reinforcement learning using evolution strategies, The 2003 Congress on Evolutionary Computation, 2003. CEC '03., pp.2588-2595, 2003.
DOI : 10.1109/CEC.2003.1299414

C. Igel, Multi-objective Model Selection for Support Vector Machines, Third International Conference on Evolutionary Multi-Criterion Optimization, pp.534-546, 2005.
DOI : 10.1007/978-3-540-31880-4_37

C. Igel and M. Toussaint, On classes of functions for which No Free Lunch results hold, Information Processing Letters, vol.86, issue.6, pp.317-321, 2003.
DOI : 10.1016/S0020-0190(03)00222-9

C. Igel, W. Erlhagen, and D. Jancke, Optimization of dynamic neural fields, Neurocomputing, vol.36, issue.1-4, pp.1-4225, 2001.
DOI : 10.1016/S0925-2312(00)00328-3

C. Igel, T. Suttorp, and N. Hansen, A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.453-460, 2006.
DOI : 10.1145/1143997.1144082

C. Igel, N. Hansen, and S. Roth, Covariance Matrix Adaptation for Multi-objective Optimization, Evolutionary Computation, vol.15, issue.1, pp.1-28, 2007.
DOI : 10.1109/TEVC.2003.810758

C. Igel, T. Suttorp, and N. Hansen, Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES, Fourth International Conference on Evolutionary Multi- Criterion Optimization, pp.171-185, 2007.
DOI : 10.1007/978-3-540-70928-2_16

A. Iorio and X. Li, Solving Rotated Multi-objective Optimization Problems Using Differential Evolution, Proceeding of the 17th Joint Australian Conference on Artificial Intelligence, pp.861-872, 2005.
DOI : 10.1007/978-3-540-30549-1_74

J. Jägersküpper, How the (1+1) ES using isotropic mutations minimizes positive definite quadratic forms, Theoretical Computer Science, vol.36, issue.1, pp.38-56, 2006.

J. Jägersküpper, Lower Bounds for Hit-and-Run Direct Search, Proceedings of the Fourth International Symposium on Stochastic Algorithms: Foundations and Applications (SAGA), pp.118-129, 2007.

J. Jägersküpper, Lower bounds for randomized direct search with isotropic sampling, Operations Research Letters, vol.36, issue.3, pp.327-332, 2008.
DOI : 10.1016/j.orl.2007.10.003

Y. Jin, T. Jones, and S. Forrest, Multi-Objective Machine Learning Fitness distance correlation as a measure of problem difficulty for genetic algorithms, Proceedings of the 6th International Conference on Genetic Algorithms, pp.184-192, 1995.

Y. Kassahun and G. Sommer, Efficient reinforcement learning through evolutionary acquisition of neural topologies, 13th European Symposium on Artificial Neural Networks (ESANN 2005), d-side, pp.259-266, 2005.

S. Kern, S. Müller, N. Hansen, D. Büche, J. Ocenasek et al., Learning probability distributions in continuous evolutionary algorithms ??? a comparative review, Natural Computing, vol.3, issue.1, pp.77-112, 2004.
DOI : 10.1023/B:NACO.0000023416.59689.4e

V. Klee, Can the Measure of ??? n 1 [ a i , b i ] be Computed in Less Than O(n logn) Steps?, The American Mathematical Monthly, vol.84, issue.4, pp.284-285, 1977.
DOI : 10.2307/2318871

J. Knight and M. Lunacek, Reducing the space-time complexity of the CMA-ES A comparison of evolution strategies and backpropagation for neural network training, Proceedings of the Genetic and Evolutionary Computation Conference, pp.1-487, 2002.

B. Mersch, T. Glasmachers, P. Meinicke, and C. Igel, EVOLUTIONARY OPTIMIZATION OF SEQUENCE KERNELS FOR DETECTION OF BACTERIAL GENE STARTS, special issue on selected papers presented at the International Conference on Artificial Neural Networks, pp.369-381, 2006.
DOI : 10.1142/S0129065707001214

I. Mierswa, Evolutionary learning with kernels, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.1553-1560, 2006.
DOI : 10.1145/1143997.1144249

I. Mierswa, Controlling overfitting with multi-objective support vector machines, Proceedings of the 9th annual conference on Genetic and evolutionary computation , GECCO '07, pp.1830-1837, 2007.
DOI : 10.1145/1276958.1277323

K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer's International Series in Operations Research & Management Science Kluwer Academic Pub- lishers Ostermeier A (1992) An evolution strategy with momentum adaptation of the random number distribution. Parallel Problem Solving from, Nature, vol.12, issue.2, pp.197-206, 1999.

M. Overmars and C. Yap, New Upper Bounds in Klee???s Measure Problem, SIAM Journal on Computing, vol.20, issue.6, pp.1034-1045, 1991.
DOI : 10.1137/0220065

A. Pellecchia, C. Igel, J. Edelbrunner, and G. Schöner, Making Driver Modeling Attractive, IEEE Intelligent Systems, vol.20, issue.2, pp.8-12, 2005.
DOI : 10.1109/MIS.2005.31

J. Poland, A. Zell, L. Spector, E. Goodman, A. Wu et al., Main vector adaptation: A CMA variant with linear time and space complexity, Proceedings of the Genetic and Evolutionary Computation Conference, pp.1050-1055, 2001.

I. Rechenberg, R. Frommann-holzboog-ros, and N. Hansen, Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution A simple modification in CMA-ES achieving linear time and space complexity, ) Parallel Problem Solving from Nature (PPSN X), pp.296-305, 1973.

G. Rudolph, On correlated mutations in evolution strategies, Parallel Problem Solving from Nature 2 (PPSN II), pp.105-114, 1992.

T. Runarsson and S. Sigurdsson, Asynchronous parallel evolutionary model selection for support vector machines, Neural Information Processing ? Letters and Reviews, vol.3, issue.3, pp.59-68, 2004.

S. Schneider, C. Igel, C. Klaes, H. Dinse, and J. Wiemer, Evolutionary Adaptation of Nonlinear Dynamical Systems in Computational Neuroscience, Genetic Programming and Evolvable Machines, vol.5, issue.2, pp.215-227, 2004.
DOI : 10.1023/B:GENP.0000023689.70987.6a

M. Schumer and K. Steiglitz, Adaptive step size random search, IEEE Transactions on Automatic Control, vol.13, issue.3, pp.270-276, 1968.
DOI : 10.1109/TAC.1968.1098903

H. Schwefel, Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series, 1995.

N. Siebel and G. Sommer, Evolutionary reinforcement learning of artificial neural networks, International Journal of Hybrid Intelligent Systems, vol.4, issue.3, pp.171-183, 2007.
DOI : 10.3233/HIS-2007-4304

R. Sutton, A. Barto, T. Suttorp, and C. Igel, Reinforcement Learning: An Introduction Multi-objective optimization of support vector machines, Multi-objective Machine Learning, pp.199-220, 1998.
DOI : 10.1007/978-1-4615-3618-5

T. Voß, N. Beume, G. Rudolph, and C. Igel, Scalarization versus indicator-based selection in multi-objective CMA evolution strategies, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.3041-3048, 2008.
DOI : 10.1109/CEC.2008.4631208

D. Whitley, M. Lunacek, and A. Sokolov, Comparing the Niches of CMA-ES, CHC and Pattern Search Using Diverse Benchmarks, Parallel Problem Solving from Nature (PPSN IX), pp.988-997, 2006.
DOI : 10.1007/11844297_100

S. Winter, B. Brendel, I. Pechlivanis, K. Schmieder, and C. Igel, Registration of CT and Intraoperative 3-D Ultrasound Images of the Spine Using Evolutionary and Gradient-Based Methods, IEEE Transactions on Evolutionary Computation, vol.12, issue.3, pp.284-296, 2008.
DOI : 10.1109/TEVC.2007.907558

E. Zitzler and L. Thiele, Multiobjective optimization using evolutionary algorithms ??? A comparative case study, Parallel Problem Solving from Nature (PPSN V), pp.292-301, 1998.
DOI : 10.1007/BFb0056872

E. Zitzler, L. Thiele, M. Laumanns, C. Fonseca, G. Da-fonseca et al., Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758