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

N. Hansen, A. Auger, S. Finck, and R. Ros, Real-parameter black-box optimization benchmarking 2009: Experimental setup, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00362649

N. Hansen, S. Finck, R. Ros, and A. Auger, Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00362633

A. Marcello-anile, V. Cutello, G. Nicosia, R. Rascunà, and S. Spinella, Comparison among evolutionary algorithms and classical optimization methods for circuit design problems, Congress on Evolutionary Computation, pp.765-772, 2005.

D. Arnold and R. Salomon, Evolutionary Gradient Search Revisited, IEEE Transactions on Evolutionary Computation, vol.11, issue.4, pp.480-495, 2007.
DOI : 10.1109/TEVC.2006.882427

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

A. Auger, N. Hansen, J. M. Perez-zerpa, R. Ros, and M. Schoenauer, Empirical comparisons of several derivative free optimization algorithms, Acte du 9ieme colloque national en calcul des structures, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01408402

A. Auger, Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2447-2452, 2009.
DOI : 10.1145/1570256.1570342
URL : https://hal.archives-ouvertes.fr/inria-00430515

A. Auger, Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2453-2458, 2009.
DOI : 10.1145/1570256.1570343
URL : https://hal.archives-ouvertes.fr/inria-00430516

A. Auger and N. Hansen, Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2459-2466, 2009.
DOI : 10.1145/1570256.1570344
URL : https://hal.archives-ouvertes.fr/inria-00430517

A. Auger and N. Hansen, Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2467-2472, 2009.
DOI : 10.1145/1570256.1570345
URL : https://hal.archives-ouvertes.fr/inria-00430518

A. Auger and R. Ros, Benchmarking the pure random search on the BBOB-2009 testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2479-2484, 2009.
DOI : 10.1145/1570256.1570347
URL : https://hal.archives-ouvertes.fr/inria-00430532

A. Auger and R. Ros, Benchmarking the pure random search on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2485-2490, 2009.
DOI : 10.1145/1570256.1570348
URL : https://hal.archives-ouvertes.fr/inria-00430534

A. Auger, N. Hansen, J. M. Perez-zerpa, R. Ros, and M. Schoenauer, Experimental Comparisons of Derivative Free Optimization Algorithms, Lecture Notes in Computer Science, vol.5526, pp.3-15, 2009.
DOI : 10.1007/978-3-642-02011-7_3
URL : https://hal.archives-ouvertes.fr/inria-00397334

R. Bellman, Adaptive Control Processes: A Guided Tour, 1961.
DOI : 10.1515/9781400874668

G. Bilchev and I. C. Parmee, Ant colony search vs. genetic algorithms, 1995.

A. N. Peter, D. Bosman, and . Thierens, Expanding from discrete to continuous estimation of distribution algorithms: The idea, PPSN VI: Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, pp.767-776, 2000.

A. N. Peter, J. Bosman, D. Grahl, and . Thierens, AMaLGaM IDEAs in noiseless black-box optimization benchmarking, Rothlauf, pp.2247-2254, 2009.

A. N. Peter, J. Bosman, D. Grahl, and . Thierens, AMaLGaM IDEAs in noisy black-box optimization benchmarking, Rothlauf, pp.2351-2358, 2009.

S. H. Brooks, A Discussion of Random Methods for Seeking Maxima, Operations Research, vol.6, issue.2, pp.244-251, 1958.
DOI : 10.1287/opre.6.2.244

J. Brownlee, A note on research methodology and benchmarking optimization algorithms Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), Complex Intelligent Systems Laboratory (CIS), 2007.

J. Brownlee and . Oat, The optimization algorithm toolkit, Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), 2007.

C. G. Broyden, A class of methods for solving nonlinear simultaneous equations, Mathematics of Computation, vol.19, issue.92, pp.577-593, 1965.
DOI : 10.1090/S0025-5718-1965-0198670-6

C. G. Broyden, The Convergence of a Class of Double-rank Minimization Algorithms, IMA Journal of Applied Mathematics, vol.6, issue.3, pp.76-90, 1970.
DOI : 10.1093/imamat/6.3.222

M. Clerc and J. Kennedy, The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, vol.6, issue.1, pp.58-73, 2002.
DOI : 10.1109/4235.985692

A. Colorni, M. Dorigo, and V. Maniezzo, Distributed optimization by ant colonies, Proceedings of the First European Conference on Artificial Life, 1991.

A. R. Conn and P. L. Toint, An Algorithm using Quadratic Interpolation for Unconstrained Derivative Free Optimization, Nonlinear Optimization and Applications, pp.27-47, 1996.
DOI : 10.1007/978-1-4899-0289-4_3

A. R. Conn, K. Scheinberg, and P. L. Toint, Recent progress in unconstrained nonlinear optimization without derivatives, Mathematical Programming, vol.12, issue.1, pp.397-414, 1997.
DOI : 10.1007/BF02614326

A. R. Conn, K. Scheinberg, and P. L. Toint, On the convergence of derivativefree methods for unconstrained optimization Approximation Theory and Optimization: Tributes to M, J. D. Powell, pp.83-108, 1997.

A. R. Conn, K. Scheinberg, and P. L. Toint, A derivative free optimization algorithm in practice, 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1998.
DOI : 10.2514/6.1998-4718

A. R. Conn, N. I. Gould, and P. L. Toint, Trust-region methods, Society for Industrial and Applied Mathematics, 2000.
DOI : 10.1137/1.9780898719857

T. Csendes, Nonlinear parameter estimation by global optimization?efficiency and reliability, Acta Cybern, vol.8, issue.4, pp.361-370, 1989.

L. Devroye, The compund random search, International Symposium on Systems Engineering and Analysis, pp.195-110, 1972.

M. A. Diniz-ehrhardt, J. M. Martínez, and M. Raydan, A derivative-free nonmonotone line-search technique for unconstrained optimization, Journal of Computational and Applied Mathematics, vol.219, issue.2, pp.383-397, 2008.
DOI : 10.1016/j.cam.2007.07.017

L. C. Dixon, J. Gomulka, and S. E. Herson, Reflection on global optimization problems, Optimization in Action, pp.398-435, 1976.

B. Doerr, M. Fouz, M. Schmidt, and M. Wahlström, BBOB, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2239-2246, 2009.
DOI : 10.1145/1570256.1570312

B. Efron and R. Tibshirani, An introduction to the bootstrap, 1993.
DOI : 10.1007/978-1-4899-4541-9

M. El-abd and M. S. Kamel, Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2263-2268, 2009.
DOI : 10.1145/1570256.1570315

M. El-abd and M. S. Kamel, Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2269-2274, 2009.
DOI : 10.1145/1570256.1570316

M. El-abd and M. S. Kamel, Black-box optimization benchmarking for noiseless function testbed using PSO Bounds, Rothlauf, pp.2275-2280, 2009.

V. Feoktistov, Differential Evolution: In Search of Solutions, 2006.

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

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

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

R. Fletcher, Practical methods of optimization Wiley-Interscience, 1987.

K. R. Fowler, J. P. Reese, C. E. Kees, J. E. Dennis-jr, C. T. Kelley et al., Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems, Advances in Water Resources, vol.31, issue.5, pp.743-757, 2008.
DOI : 10.1016/j.advwatres.2008.01.010

M. Gallagher, Black-box optimization benchmarking, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2281-2286, 2009.
DOI : 10.1145/1570256.1570318

R. Marcus and . Gallagher, Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noisy function testbed, Rothlauf, pp.2383-2388, 2009.

C. García-martínez and M. Lozano, A continuous variable neighbourhood search based on specialised EAs, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2287-2294, 2009.
DOI : 10.1145/1570256.1570319

C. García-martínez and M. Lozano, A continuous variable neighbourhood search based on specialised eas, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2367-2374, 2009.
DOI : 10.1145/1570256.1570330

J. García-nieto, E. Alba, and J. Apolloni, Noiseless functions black-box optimization, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2231-2238, 2009.
DOI : 10.1145/1570256.1570311

J. García-nieto, E. Alba, and J. Apolloni, Particle swarm hybridized with differential evolution, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2343-2350, 2009.
DOI : 10.1145/1570256.1570327

P. Gilmore and C. T. Kelley, An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima, SIAM Journal on Optimization, vol.5, issue.2, pp.269-285, 1995.
DOI : 10.1137/0805015

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

I. M. Nicholas, D. Gould, P. L. Orban, and . Toint, Cuter and sifdec: A constrained and unconstrained testing environment, revisited, ACM Trans. Math. Softw, vol.29, issue.4, pp.373-394, 2003.

L. Grippo, S. Lampariello, and . Lucidi, A Nonmonotone Line Search Technique for Newton???s Method, SIAM Journal on Numerical Analysis, vol.23, issue.4, pp.707-716, 1986.
DOI : 10.1137/0723046

N. Hansen, The CMA Evolution Strategy: A Comparing Review, Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pp.75-102
DOI : 10.1007/3-540-32494-1_4

N. Hansen and S. Kern, 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, 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 6 th International Conference on Genetic Algorithms, pp.57-64, 1995.

N. Hansen, S. D. 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, A. Auger, S. Finck, and R. Ros, Real-parameter black-box optimization benchmarking 2009: Experimental setup, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00362649

N. Hansen, S. Finck, R. Ros, and A. Auger, Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00362633

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, Compilation of results on the 2005 CEC benchmark function set. Online, 2006.

N. Hansen, Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2389-2396, 2009.
DOI : 10.1145/1570256.1570333
URL : https://hal.archives-ouvertes.fr/inria-00382093

N. Hansen, Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2397-2402, 2009.
DOI : 10.1145/1570256.1570334
URL : https://hal.archives-ouvertes.fr/inria-00382101

N. Hansen, Benchmarking the nelder-mead downhill simplex algorithm with many local restarts, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2403-2408, 2009.
DOI : 10.1145/1570256.1570335
URL : https://hal.archives-ouvertes.fr/inria-00382104

N. Hansen, The CMA-ES evolution strategy: A tutorial. Online, 2009.

N. Hansen, R. Ros, N. Mauny, M. Schoenauer, and A. Auger, Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems, Applied Soft Computing, vol.11, issue.8, 2009.
DOI : 10.1016/j.asoc.2011.03.001
URL : https://hal.archives-ouvertes.fr/inria-00583669

J. H. Holland, Adaptation in natural and artificial systems, 1975.

R. Hooke and T. A. Jeeves, `` Direct Search'' Solution of Numerical and Statistical Problems, Journal of the ACM, vol.8, issue.2, pp.212-229, 1961.
DOI : 10.1145/321062.321069

R. Horst, Introduction to Global Optimization (Nonconvex Optimization and Its Applications), 2002.

W. Huyer and A. Neumaier, Global optimization by multilevel coordinate search, Journal of Global Optimization, vol.14, issue.4, pp.331-355, 1999.
DOI : 10.1023/A:1008382309369

W. Huyer and A. Neumaier, SNOBFIT -- Stable Noisy Optimization by Branch and Fit, ACM Transactions on Mathematical Software, vol.35, issue.2, pp.1-25, 2008.
DOI : 10.1145/1377612.1377613
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.5642

W. Huyer and A. Neumaier, Benchmarking of SNOBFIT on the noisy function testbed. Online, 2009.

W. Huyer and A. Neumaier, Benchmarking of MCS on the noisy function testbed. Online, 2009.

W. Huyer and A. Neumaier, Benchmarking of MCS on the noiseless function testbed. Online, 2009.

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

D. S. Johnson, A theoretician's guide to the experimental analysis of algorithms In Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DI- MACS Implementation Challenges, DIMACS, vol.59, pp.215-250, 2002.

G. Steven and . Johnson, The nlopt nonlinear-optimization package. WWW, 2008.

D. R. Jones, C. D. Perttunen, and B. E. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications, vol.20, issue.1, pp.157-181, 1993.
DOI : 10.1007/BF00941892

C. T. Kelley, Iterative Methods for Optimization, SIAM, 1999.
DOI : 10.1137/1.9781611970920

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

S. Kirkpatrick, C. D. Gelatt, J. , and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

N. James, M. Knight, and . Lunacek, Reducing the space-time complexity of the CMA-ES, GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp.658-665, 2007.

T. G. Kolda, R. M. Lewis, and V. Torczon, Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods, SIAM Review, vol.45, issue.3, pp.385-482, 2003.
DOI : 10.1137/S003614450242889

P. Korosec and J. Silc, A stigmergy-based algorithm for black-box optimization, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2295-2302, 2009.
DOI : 10.1145/1570256.1570320

J. Kubalik, Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2303-2308, 2009.
DOI : 10.1145/1570256.1570321

S. Kucherenko, Application of Quasi Monte Carlo Methods in Global Optimization, Global Optimization, pp.111-133, 2006.
DOI : 10.1007/0-387-30528-9_5

C. Jeffrey, J. A. Lagarias, M. H. Reeds, P. E. Wright, and . Wright, Convergence properties of the nelder?mead simplex method in low dimensions

W. B. Langdon and R. Poli, Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms, IEEE Transactions on Evolutionary Computation, vol.11, issue.5, pp.561-578, 2007.
DOI : 10.1109/TEVC.2006.886448

P. Larrañaga and J. A. Lozano, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, 2001.
DOI : 10.1007/978-1-4615-1539-5

R. Lougee-heimer, The Common Optimization INterface for Operations Research: Promoting open-source software in the operations research community, IBM Journal of Research and Development, vol.47, issue.1, pp.57-66, 2003.
DOI : 10.1147/rd.471.0057

M. Lunacek, D. Whitley, and A. Sutton, The Impact of Global Structure on Search, Proceedings of the 10th international conference on Parallel Problem Solving from Nature, pp.498-507, 2008.
DOI : 10.1007/978-3-540-87700-4_50

K. I. Mckinnon, Convergence of the Nelder--Mead Simplex Method to a Nonstationary Point, SIAM Journal on Optimization, vol.9, issue.1, pp.148-158, 1998.
DOI : 10.1137/S1052623496303482

D. Molina, M. Lozano, and F. Herrera, A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noise free functions, Rothlauf, pp.2255-2262, 2009.

D. Molina, M. Lozano, and F. Herrera, A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2359-2366, 2009.
DOI : 10.1145/1570256.1570329

J. J. Moré and S. M. Wild, Benchmarking Derivative-Free Optimization Algorithms, SIAM Journal on Optimization, vol.20, issue.1, 2008.
DOI : 10.1137/080724083

J. J. Moré and S. M. Wild, Benchmarking Derivative-Free Optimization Algorithms, SIAM Journal on Optimization, vol.20, issue.1, pp.172-191, 2009.
DOI : 10.1137/080724083

H. Mühlenbein and G. Paaß, From recombination of genes to the estimation of distributions I. Binary parameters, PPSN IV: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, pp.178-187, 1996.
DOI : 10.1007/3-540-61723-X_982

C. L. Müller and I. F. Sbalzarini, A tunable real-world multi-funnel benchmark problem for evolutionary optimization (and why parallel island models might remedy the failure of CMA-ES on it), Proc. Intl. Conf. Evolutionary Computation (ICEC), 2009.

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

L. George, L. A. Nemhauser, and . Wolsey, Integer and combinatorial optimization, 1988.

M. Nicolau, Application of a simple binary genetic algorithm to a noiseless testbed benchmark, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2473-2478, 2009.
DOI : 10.1145/1570256.1570346
URL : https://hal.archives-ouvertes.fr/inria-00377093

J. Nocedal and S. J. Wright, Numerical Optimization, 2006.
DOI : 10.1007/b98874

A. Ostermeier, A. Gawelczyk, and N. Hansen, Step-size Adaptation Based on Nonlocal Use of Selection Information, Proceedings of the 3 rd Conference on Parallel Problems Solving from Nature, pp.189-198, 1994.

L. Pál, T. Csendes, M. C. Markót, and A. Neumaier, BBObenchmarking of the GLOBAL method for the noisy function testbed. Online, 2009.

L. Pál, T. Csendes, M. C. Markót, and A. Neumaier, BBObenchmarking of the GLOBAL method for the noiseless function testbed. Online, 2009.

O. Miroslav?norekmiroslav?miroslav?norek-pavel-kordík and . Ková?ík, Optimization of models: Looking for the best strategy, Proceedings of 6th EUROSIM Congress on Modelling and Simulation, Ljubjana, 2007.

M. Pelikan, D. E. Goldberg, and F. G. Lobo, A survey of optimization by building and using probabilistic models, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), pp.5-20, 2002.
DOI : 10.1109/ACC.2000.879173

D. János and . Pintér, Global Optimization in Action, Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications, volume 6 of Nonconvex Optimization and Its Applications, 1996.

J. Poland, A. Z. , E. D. Goodman, A. Wu, W. B. Langdon et al., Main vector adaptation: A CMA variant with linear time and space complexity, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp.1050-1055, 2001.

P. Posik, BBOB-benchmarking a simple estimation of distribution algorithm with Cauchy distribution, Rothlauf, pp.2309-2314, 2009.

P. Posik, BBOB-benchmarking the DIRECT global optimization algorithm, Rothlauf, pp.2315-2320, 2009.

P. Posik, BBOB-benchmarking the generalized generation gap model with parent centric crossover, Rothlauf, pp.2321-2328, 2009.

P. Posik, BBOB-benchmarking two variants of the line-search algorithm, Rothlauf, pp.2329-2336, 2009.

P. Posik, BBOB-benchmarking the Rosenbrock's local search algorithm, Rothlauf, pp.2337-2342, 2009.

M. J. Powell, The NEWUOA software for unconstrained optimization without derivatives. Large Scale Nonlinear Optimization, pp.255-297, 2006.

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

K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution, 2005.
DOI : 10.1007/978-3-642-30504-7_8

K. V. Price, Differential evolution: a fast and simple numerical optimizer, Proceedings of North American Fuzzy Information Processing, pp.524-527, 1996.
DOI : 10.1109/NAFIPS.1996.534790

I. Rechenberg and . Evolutionsstrategie, Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.

R. Ros, Benchmarking the BFGS algorithm on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2409-2414, 2009.
DOI : 10.1145/1570256.1570336
URL : https://hal.archives-ouvertes.fr/inria-00377076

R. Ros, Benchmarking the BFGS algorithm on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2415-2420, 2009.
DOI : 10.1145/1570256.1570337
URL : https://hal.archives-ouvertes.fr/inria-00377079

R. Ros, Benchmarking the NEWUOA on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2421-2428, 2009.
DOI : 10.1145/1570256.1570338
URL : https://hal.archives-ouvertes.fr/inria-00377082

R. Ros, Benchmarking the NEWUOA on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2429-2434, 2009.
DOI : 10.1145/1570256.1570339
URL : https://hal.archives-ouvertes.fr/inria-00377083

R. Ros, Benchmarking sep-CMA-ES on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2435-2440, 2009.
DOI : 10.1145/1570256.1570340
URL : https://hal.archives-ouvertes.fr/inria-00377087

R. Ros, Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, GECCO '09, pp.2441-2446, 2009.
DOI : 10.1145/1570256.1570341
URL : https://hal.archives-ouvertes.fr/inria-00377090

R. Ros and N. Hansen, A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity, Parallel Problem Solving from Nature -PPSN X, 10th International Conference Dortmund Proceedings, pp.296-305, 2008.
DOI : 10.1007/978-3-540-87700-4_30
URL : https://hal.archives-ouvertes.fr/inria-00287367

R. Ros and N. Hansen, A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity, Research Report, vol.6498
DOI : 10.1007/978-3-540-87700-4_30
URL : https://hal.archives-ouvertes.fr/inria-00287367

M. Schumer and K. Steiglitz, Adaptive step size random search. Automatic Control, IEEE Transactions on, vol.13, pp.270-276, 1968.
DOI : 10.1109/tac.1968.1098903
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.9779

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

M. Sebag and A. Ducoulombier, Extending population-based incremental learning to continuous search spaces, PPSN V: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp.418-427, 1998.
DOI : 10.1007/BFb0056884
URL : https://hal.archives-ouvertes.fr/hal-00116542

Y. Shang and Y. Qiu, A Note on the Extended Rosenbrock Function, Evolutionary Computation, vol.14, issue.1, pp.119-126, 2006.
DOI : 10.1109/4235.771163

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

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

Y. Shi, E. D. Eberhart, and . Center, Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999.
DOI : 10.1109/CEC.1999.785511

K. Socha and M. Dorigo, Ant colony optimization for continuous domains, European Journal of Operational Research, vol.185, issue.3, pp.1155-1173, 2008.
DOI : 10.1016/j.ejor.2006.06.046

J. Spall, S. Hill, and D. Stark, Theoretical Framework for Comparing Several Stochastic Optimization Approaches, Probabilistic and Randomized Methods for Design under Uncertainty, pp.99-117, 2006.
DOI : 10.1007/1-84628-095-8_3

C. James and . Spall, Introduction to Stochastic Search and Optimization, 2003.

R. Storn, On the usage of differential evolution for function optimization, Proceedings of North American Fuzzy Information Processing, pp.519-523, 1996.
DOI : 10.1109/NAFIPS.1996.534789

R. Storn and K. V. Price, Differential evolution -a simple and efficient adaptive scheme for global optimization over continuous spaces, 1995.

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.341-3591008202821328, 1997.
DOI : 10.1023/A:1008202821328

P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen et al., Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, 2005.

T. Suttorp, N. Hansen, and C. Igel, Efficient covariance matrix update for variable metric evolution strategies, Machine Learning, pp.167-197, 2009.
DOI : 10.1007/s10994-009-5102-1
URL : https://hal.archives-ouvertes.fr/inria-00369468

A. Törn and A. Zilinskas, Global optimization, 1989.
DOI : 10.1007/3-540-50871-6

A. Wächter, T. Lorenz, and . Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, vol.10, issue.1, pp.25-57, 2006.
DOI : 10.1007/s10107-004-0559-y

P. , P. Wang, and D. Chen, Continuous optimization by a variant of simulated annealing, Comput. Optim. Appl, vol.6, issue.1, pp.59-71, 1996.