B. Arnold, D. V. Arnold, and H. Beyer, A general noise model and its effects on evolution strategy performance, IEEE Transactions on Evolutionary Computation, vol.10, issue.4, pp.380-391, 2006.
DOI : 10.1109/TEVC.2005.859467

A. Morales, Noisy optimization convergence rates, Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, GECCO '13 Companion, pp.223-224, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00863584

K. Astrom, Optimal control of Markov processes with incomplete state information, Journal of Mathematical Analysis and Applications, vol.10, issue.1, pp.174-205, 1965.
DOI : 10.1016/0022-247X(65)90154-X

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

H. Auger, 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

. Auger, Experimental Comparisons of Derivative Free Optimization Algorithms, 8th International Symposium on Experimental Algorithms, pp.3-15, 2009.
DOI : 10.1109/CEC.2005.1554903

URL : https://hal.archives-ouvertes.fr/inria-00397334

. Balsa-canto, Global Optimization in Systems Biology: Stochastic Methods and Their Applications, pp.409-424, 2012.
DOI : 10.1007/978-1-4419-7210-1_24

J. R. Banga, Optimization in computational systems biology, BMC Systems Biology, vol.2, issue.1, p.47, 2008.
DOI : 10.1186/1752-0509-2-47

URL : https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-2-47?site=bmcsystbiol.biomedcentral.com

P. Baudis, P. Baudis, and P. Posik, Online Black-Box Algorithm Portfolios for Continuous Optimization, Proceedings of PPSN, pp.40-49, 2014.
DOI : 10.1007/978-3-319-10762-2_4

R. Bellman, Dynamic Programming, 1957.

Y. Bengio, Using a Financial Training Criterion Rather than a Prediction Criterion, CIRANO Working Papers 98s-21, 1998.
DOI : 10.1162/neco.1989.1.4.425

. Berthier and V. Berthier, Comparing optimizers on a unit commitment problem, In Artificial Evolution, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01215804

. Berthier and V. Berthier, Experiments on the CEC 2015 expensive optimization testbed, 2015 IEEE Congress on Evolutionary Computation (CEC), pp.1059-1066, 2015.
DOI : 10.1109/CEC.2015.7257007

URL : https://hal.archives-ouvertes.fr/hal-01215805

. Berthier and V. Berthier, Progressive Differential Evolution on Clustering Real World Problems, International Conference on Artificial Evolution (Evolution Artificielle), pp.71-82, 2015.
DOI : 10.1007/978-3-319-31471-6_6

URL : https://hal.archives-ouvertes.fr/hal-01215803

. Berthier and V. Berthier, Progressive Differential Evolution on Clustering Real World Problems, International Conference on Artificial Evolution (Evolution Artificielle), pp.71-82, 2015.
DOI : 10.1007/978-3-319-31471-6_6

URL : https://hal.archives-ouvertes.fr/hal-01215803

. Berthier, Combining policies: the best of human expertise and neurocontrol, Artificial Evolution 2015, p.To?appear, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01194516

. Berthier, . Teytaud, V. Berthier, and O. Teytaud, On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables, International Conference on Artificial Evolution (Evolution Artificielle), pp.234-247, 2015.
DOI : 10.1007/978-3-319-31471-6_18

URL : https://hal.archives-ouvertes.fr/hal-01194519

. Berthier, . Teytaud, V. Berthier, and O. Teytaud, Sieves method in fuzzy control: logarithmically increase the number of rules, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp.1-9, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01215806

H. Beyer, The Theory of Evolution Strategies, Natural Computing Series, 2001.
DOI : 10.1007/978-3-662-04378-3

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

URL : https://hal.archives-ouvertes.fr/halshs-01664458

S. Beyer, H. Beyer, and B. Sendhoff, Covariance Matrix Adaptation Revisited ??? The CMSA Evolution Strategy ???, Proceedings of PPSN, pp.123-132, 2008.
DOI : 10.1007/978-3-540-87700-4_13

URL : http://www2.staff.fh-vorarlberg.ac.at/~hgb/New-Papers/PPSN08_BS08a.pdf

. Birge, J. R. Kärtner-]-birge, and F. X. Kärtner, Efficient analytic computation of dispersion from multilayer structures, Applied Optics, vol.45, issue.7, pp.451478-1483, 2006.
DOI : 10.1364/AO.45.001478

. Booker, A rigorous framework for optimization of expensive functions by surrogates . Structural Optimization, pp.1-13, 1999.

B. Bragg, W. H. Bragg, and W. L. Bragg, The reflection of x-rays by crystals, Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, pp.88428-438, 1913.

. Brest, Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems, IEEE Transactions on Evolutionary Computation, vol.10, issue.6, pp.646-657, 2006.
DOI : 10.1109/TEVC.2006.872133

C. G. Broyden, The Convergence of a Class of Double-rank Minimization Algorithms, IMA Journal of Applied Mathematics, vol.6, issue.3, pp.222-231, 1970.
DOI : 10.1093/imamat/6.3.222

. Bubeck, Pure exploration in finitely-armed and continuous-armed bandits, Theoretical Computer Science, vol.412, issue.19, pp.4121832-1852, 2011.
DOI : 10.1016/j.tcs.2010.12.059

URL : https://hal.archives-ouvertes.fr/hal-00609550

. Cauwet, Algorithm Portfolios for Noisy Optimization: Compare Solvers Early, International Conference on Learning and Intelligent Optimization, pp.1-15, 2014.
DOI : 10.1007/978-3-319-09584-4_1

URL : https://hal.archives-ouvertes.fr/hal-00926638

. Chaslot, Progressive Strategies for Monte-Carlo Tree Search, Proceedings of the 10th Joint Conference on Information Sciences, pp.655-661, 2007.

. Chatterjee, Genetic algorithms and their statistical applications: an introduction, Computational Statistics & Data Analysis, vol.22, issue.6, pp.22633-651, 1996.
DOI : 10.1016/0167-9473(96)00011-4

. Conn, Convergence of quasi-Newton matrices generated by the symmetric rank one update, Mathematical Programming, pp.1-3177, 1991.
DOI : 10.1007/BF01594934

R. Coulom, Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Proceedings of the 5th International Conference on Computers and Games, pp.72-83, 2006.
DOI : 10.1007/978-3-540-75538-8_7

URL : https://hal.archives-ouvertes.fr/inria-00116992

S. Das, S. Das, and P. N. Suganthan, Differential Evolution: A Survey of the State-of-the-Art, IEEE Transactions on Evolutionary Computation, vol.15, issue.1, pp.4-31, 2011.
DOI : 10.1109/TEVC.2010.2059031

W. Davidon, Variable metric algorithm for minimization, 1959.
DOI : 10.2172/4252678

URL : http://www.cs.berkeley.edu/%7Ejduchi/papers/Davidon91.pdf

. Devroye, A probabilistic Theory of Pattern Recognition, 1997.
DOI : 10.1007/978-1-4612-0711-5

S. Doya, K. Doya, and K. Samejima, Multiple Model-Based Reinforcement Learning, Neural Computation, vol.3, issue.6, pp.1347-1369, 2002.
DOI : 10.1016/S1364-6613(98)01221-2

M. Du, An Interior Point Algorithm for Minimum Sum-of-Squares Clustering, SIAM Journal on Scientific Computing, vol.21, issue.4, pp.1485-1505, 1999.

K. Eberhart, R. Eberhart, and J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.
DOI : 10.1109/MHS.1995.494215

. El-beltagy, Metamodeling techniques for evolutionary optimization of computationally expensive problems: Promises and limitations, Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, pp.196-203, 1999.

V. Fabian, Stochastic Approximation of Minima with Improved Asymptotic Speed, The Annals of Mathematical Statistics, vol.38, issue.1, pp.191-200, 1967.
DOI : 10.1214/aoms/1177699070

URL : http://doi.org/10.1214/aoms/1177699070

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

R. Fletcher, A new approach to variable metric algorithms, The Computer Journal, vol.13, issue.3, pp.317-322, 1970.
DOI : 10.1093/comjnl/13.3.317

URL : https://academic.oup.com/comjnl/article-pdf/13/3/317/988678/130317.pdf

T. Fournier, H. Fournier, and O. Teytaud, Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign Patterns, Algorithmica, vol.XVI, issue.2, pp.387-408, 2011.
DOI : 10.1145/1570256.1570430

URL : https://hal.archives-ouvertes.fr/inria-00452791

M. Schmidhuber and J. , A neural network model for inter-problem adaptive online time allocation, Artificial Neural Networks: Formal Models and Their Applications?ICANN 2005, pp.752-752, 2005.

M. Schmidhuber and J. , Learning dynamic algorithm portfolios, Annals of Mathematics and Artificial Intelligence, vol.47, pp.3-4295, 2006.

M. Gallagher, Towards improved benchmarking of black-box optimization algorithms using clustering problems, Soft Computing, vol.8, issue.2, pp.3835-3849, 2016.
DOI : 10.1109/TNN.2005.845141

. Gardner, A speculative approach to parallelization in particle swarm optimization, Swarm Intelligence, vol.1, issue.1, pp.77-116, 2012.
DOI : 10.1109/CEC.2009.4983119

. Gelly, Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime, Evolutionary Computation, vol.26, issue.3, pp.411-434, 2007.
DOI : 10.1137/0801010

URL : https://hal.archives-ouvertes.fr/inria-00173221

F. Girosi, An Equivalence Between Sparse Approximation and Support Vector Machines, Proc. NIPS 10, pp.1455-1480, 1998.
DOI : 10.1007/BF01437407

D. Goldfarb, A family of variable-metric methods derived by variational means, Mathematics of Computation, vol.24, issue.109, pp.23-26, 1970.
DOI : 10.1090/S0025-5718-1970-0258249-6

. Gould, CUTEr and SifDec, ACM Transactions on Mathematical Software, vol.29, issue.4, pp.373-394, 2003.
DOI : 10.1145/962437.962439

Y. Hamadi, Search: from Algorithms to Systems (HDR). Habilitationàtation`tationà diriger des recherches, 2013.
DOI : 10.1007/978-3-642-41482-4

N. Hansen, Adaptive Encoding for Optimization, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00275983

. Hansen, Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1689-1696, 2010.
DOI : 10.1145/1830761.1830790

URL : https://hal.archives-ouvertes.fr/hal-00545727

. Hansen, Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1689-1696, 2010.
DOI : 10.1145/1830761.1830790

URL : https://hal.archives-ouvertes.fr/hal-00545727

O. Hansen, 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

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2003.
DOI : 10.1016/0004-3702(95)00124-7

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

. Hansen, PSO Facing Non-Separable and Ill-Conditioned Problems, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00250078

. Hart, External Reflection from Omnidirectional Dielectric Mirror Fibers, Science, vol.296, issue.5567, pp.296510-513, 2002.
DOI : 10.1126/science.1070050

V. Heidrich-meisner and C. Igel, Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.401-408, 2009.
DOI : 10.1145/1553374.1553426

J. Jägersküpper, In between progress rate and stochastic convergence. Dagstuhl's seminar, 2006.

. Jägersküpper, . Witt, J. Jägersküpper, and C. Witt, Rigorous runtime analysis of a (µ+ 1) es for the sphere function, Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp.849-856, 2005.

. Jamieson, Query complexity of derivative-free optimization, NIPS, pp.2681-2689, 2012.

. Jones, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

. Kearns, A sparse sampling algorithm for near-optimal planning in large markov decision processes, IJCAI, pp.1324-1231, 1999.

. Keijzer, Evolving Objects: A General Purpose Evolutionary Computation Library, Artificial Evolution, number 2310 in Lecture Notes in Computer Science, pp.231-242, 2002.
DOI : 10.1007/3-540-46033-0_19

URL : http://www.eeaax.polytechnique.fr/EO/EO-EA01.ps.gz

. Keller, Solid-state low-loss intracavity saturable absorber for nd: Ylf lasers: an antiresonant semiconductor fabry?perot saturable absorber, Optics letters, issue.7, pp.17505-507, 1992.

E. Kennedy, J. Kennedy, and R. C. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

. Kinoshita, Physics of structural colors, Reports on Progress in Physics, vol.71, issue.7, p.71076401, 2008.
DOI : 10.1088/0034-4885/71/7/076401

. Kleinman, Simulation-Based Optimization with Stochastic Approximation Using Common Random Numbers, Management Science, vol.45, issue.11, pp.451570-1578, 1999.
DOI : 10.1287/mnsc.45.11.1570

L. Kocis and W. J. Whiten, Computational investigations of low-discrepancy sequences, ACM Transactions on Mathematical Software, vol.23, issue.2, pp.266-294, 1997.
DOI : 10.1145/264029.264064

S. Kocsis, L. Kocsis, and C. Szepesvari, Bandit Based Monte-Carlo Planning, 15th European Conference on Machine Learning (ECML), pp.282-293, 2006.
DOI : 10.1007/11871842_29

URL : http://zaphod.aml.sztaki.hu/papers/ecml06.pdf

. Latorre, Large scale global optimization: Experimental results with MOS-based hybrid algorithms, 2013 IEEE Congress on Evolutionary Computation, pp.2742-2749, 2013.
DOI : 10.1109/CEC.2013.6557901

L. Liu, J. Liu, and J. Lampinen, A Fuzzy Adaptive Differential Evolution Algorithm, Soft Computing, vol.9, issue.6, pp.448-462, 2005.
DOI : 10.1007/s00500-004-0363-x

. Mahdad, Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch, IEEE Proceedings of the 2010 IEEE International Conference of the IEEE Industrial Electronics Society, pp.2695-2701, 2010.
DOI : 10.1109/iecon.2010.5675112

URL : https://hal.archives-ouvertes.fr/hal-00564733

L. Marivate, V. N. Marivate, and M. L. Littman, An ensemble of linearly combined reinforcement-learning agents, AAAI (Late-Breaking Develop- ments), 2013.

J. Matou?ek, On theL2-Discrepancy for Anchored Boxes, Journal of Complexity, vol.14, issue.4, pp.527-556, 1998.
DOI : 10.1006/jcom.1998.0489

. Mcnabb, Parallel PSO using MapReduce, 2007 IEEE Congress on Evolutionary Computation, pp.7-14, 2007.
DOI : 10.1109/CEC.2007.4424448

. Monicka, Performance Evaluation of Membership Functions on Fuzzy Logic Controlled AC Voltage Controller for Speed Control of Induction Motor Drive, International Journal of Computer Applications, vol.13, issue.5, pp.8-12, 2011.
DOI : 10.5120/1778-2451

M. Nelder, J. A. Nelder, and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

R. Nowak, M. P. Nowak, and W. Römisch, Stochastic lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty, Annals of Operations Research, vol.100, pp.1-4251, 2000.

. Nudelman, Understanding Random SAT: Beyond the Clauses-to-Variables Ratio, Principles and Practice of Constraint Programming CP, pp.438-452, 2004.
DOI : 10.1007/978-3-540-30201-8_33

[. Okten and G. , Random sampling from low-discrepancy sequences: applications to option pricing, Mathematical and computer modelling, vol.35, pp.11-121221, 2002.

P. Pereira, M. V. Pereira, and L. M. Pinto, Multi-stage stochastic optimization applied to energy planning, Mathematical Programming, vol.4, issue.1-3, pp.359-375, 1991.
DOI : 10.1007/BF01582895

K. Poaík, P. Poaík, and V. Klema, JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion '12, pp.197-204, 2012.
DOI : 10.1145/2330784.2330814

. Poloni, . Pediroda, C. Poloni, and V. Pediroda, GA coupled with computationaly expensive simulations: tools to improve efficiency, Genetic Algorithms and Evolution Strategies in Engineering and Computer Sciences, pp.267-288, 1997.

M. J. Powell, Developments of NEWUOA for minimization without derivatives, IMA Journal of Numerical Analysis, vol.28, issue.4, pp.649-664, 2008.
DOI : 10.1093/imanum/drm047

W. Powell, Approximate Dynamic Programming, 2007.

K. V. Price, An introduction to differential evolution, New ideas in optimization, pp.79-108, 1999.

T. Pulina, L. Pulina, and A. Tacchella, A self-adaptive multi-engine solver for quantified Boolean formulas, Constraints, vol.2, issue.1, pp.80-116, 2009.
DOI : 10.1016/S0004-3702(02)00375-2

URL : http://ai.uwaterloo.ca/~vanbeek/Constraints/Papers/PulinaT09.pdf

P. Ratitch, B. Ratitch, and D. Precup, Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning, ECML, pp.347-358, 2004.
DOI : 10.1007/978-3-540-30115-8_33

. Rechenberg and I. Rechenberg, Evolution strategy: Optimization of technical systems by means of biological evolution, p.104, 1973.

. Rechenberg and I. Rechenberg, Evolutionstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, 1973.

E. H. Ruspini, Numerical methods for fuzzy clustering, Information Sciences, vol.2, issue.3, pp.319-350, 1970.
DOI : 10.1016/S0020-0255(70)80056-1

M. Samulowitz, H. Samulowitz, and R. Memisevic, Learning to solve qbf, Proceedings of the 22nd National Conference on Artificial Intelligence, pp.255-260, 2007.

. Schoenauer, Divide-andevolve : une nouvelle méta-heuristique pour la planification temporelle indépendante du domaine, Journées Francophones Planification, 2006.

. Schutte, Parallel global optimization with the particle swarm algorithm, International Journal for Numerical Methods in Engineering, vol.28, issue.13, pp.2296-2315, 2003.
DOI : 10.2172/378910

H. Schwefel, Numerische optimierung von computermodellen mittels der evolutionsstrategie, 1977.
DOI : 10.1007/978-3-0348-5927-1

H. Schwefel, Numerical optimization of computer models, 1981.

H. Schwefel, Numerical Optimization of Computer Models, 1981.

D. F. Shanno, Conditioning of quasi-Newton methods for function minimization, Mathematics of Computation, vol.24, issue.111, pp.647-656, 1970.
DOI : 10.1090/S0025-5718-1970-0274029-X

. Shen, . Wong, X. Shen, and W. Wong, Convergence rate of sieve estimates. The Annals of Statistics, pp.580-615, 1994.
DOI : 10.1214/aos/1176325486

URL : http://doi.org/10.1214/aos/1176325486

. Shi, . Eberhart, Y. Shi, and R. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69-73, 1998.
DOI : 10.1109/ICEC.1998.699146

. Shi, . Eberhart, Y. Shi, and R. C. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69-73, 1998.
DOI : 10.1109/ICEC.1998.699146

H. Spath, Cluster analysis algorithms for data reduction and classification of objects, 1980.

. St-pierre, Online Sparse Bandit for Card Games, Proceedings of Advanced in Computer Games 2011, pp.295-305, 2011.
DOI : 10.1007/978-3-642-31866-5_25

. Stalph, Genetic and evolutionary computation conference, gecco 2008, proceedings, atlanta, ga, usa, july 12-16, pp.535-536, 2008.

P. Storn, R. Storn, and K. Price, Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol.11, issue.4, pp.341-359, 1997.
DOI : 10.1023/A:1008202821328

M. Strens and A. Moore, Direct policy search using paired statistical tests, Proceedings of the 18th International Conference on Machine Learning, pp.545-552, 2001.

. Strens, Policy search using paired comparisons, Journal of Machine Learning Research, pp.921-950, 2002.

. Suganthan, Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, 2005.

R. Sutton, Generalization in reinforcement learning: Successful examples using sparse coarse coding, Advances in Neural Information Processing Systems, pp.1038-1044, 1996.

G. Teytaud, O. Teytaud, and S. Gelly, DCMA, Proceedings of the 9th annual conference on Genetic and evolutionary computation , GECCO '07, pp.955-963, 2007.
DOI : 10.1145/1276958.1277150

URL : https://hal.archives-ouvertes.fr/inria-00173207

. Teytaud, On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy, PPSN, pp.32-41, 2006.
DOI : 10.1007/11844297_4

URL : https://hal.archives-ouvertes.fr/inria-00112816

B. Tuffin, Randomization of quasi-monte carlo methods for error estimation: Survey and normal approximation. Monte Carlo Methods and Applications mcma, pp.3-4617, 2004.

V. Underwood, J. Underwood, and D. Vaughan, X-ray data booklet. Center for X-ray Optics, pp.4-5, 1986.

H. P. Van-hasselt-]-van-hasselt, Insights in Reinforcement Learning: formal analysis and empirical evaluation of temporal-difference learning algorithms, 2011.

V. Vapnik, The nature of statistical learning theory, 2013.

. Villemonteix, An informational approach to the global optimization of expensive-to-evaluate functions, Journal of Global Optimization, vol.10, issue.5, 2009.
DOI : 10.1007/978-1-4612-1494-6

URL : https://hal.archives-ouvertes.fr/hal-00354262

. Wang, Algorithms for infinitely many-armed bandits, Advances in Neural Information Processing Systems, pp.1729-1736, 2009.

Y. , Z. Yu, W. Zhang, and J. , Multi-population differential evolution with adaptive parameter control for global optimization, Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp.1093-1098, 2011.
DOI : 10.1145/2001576.2001724

L. A. Zadeh, THE BIRTH AND EVOLUTION OF FUZZY LOGIC*, International Journal of General Systems, vol.17, issue.2-3, pp.95-105, 1990.
DOI : 10.1080/03081079008935102

. Zambelli, NEWAVE versus ODIN: comparison of stochastic and deterministic models for the long term hydropower scheduling of the interconnected brazilian system, Sba: Controle & Automa????o Sociedade Brasileira de Automatica, vol.121, issue.5, pp.22598-609, 2011.
DOI : 10.1029/WR016i002p00275

. Zhang, Evolutionary Induction of Sparse Neural Trees, Evolutionary Computation, vol.1, issue.4, pp.213-236, 1997.
DOI : 10.1214/aos/1176350051

B. Zhao, J. Zhao, and B. K. Bose, Evaluation of membership functions for fuzzy logic controlled induction motor drive, pp.229-234, 2002.