D. W. Aha-]-aha, Generalizing from Case Studies: A Case Study, Proceedings of the Ninth International Conference on Machine Learning, pp.1-10, 1992.
DOI : 10.1016/B978-1-55860-247-2.50006-1

W. Aizawa, A. N. Aizawa, and B. W. Wah, Dynamic control of genetic algorithms in a noisy environment, Proceedings of the Fifth International Conference on Genetic Algorithms, pp.48-55, 1993.

W. Aizawa, A. N. Aizawa, and B. W. Wah, Scheduling of Genetic Algorithms in a Noisy Environment, Evolutionary Computation, vol.5, issue.2, pp.97-122, 1994.
DOI : 10.1109/69.166988

. Akimoto, Analysis of runtime of optimization algorithms for noisy functions over discrete codomains, Theoretical Computer Science, vol.605, pp.42-50, 2015.
DOI : 10.1016/j.tcs.2015.04.008
URL : https://hal.archives-ouvertes.fr/hal-01194556

S. Arlot, V-fold cross-validation improved: V-fold penalization, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00239182

C. Arlot, S. Arlot, and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054
URL : https://hal.archives-ouvertes.fr/hal-00407906

. Armstrong, Dynamic Algorithm Selection Using Reinforcement Learning, 2006 International Workshop on Integrating AI and Data Mining, pp.18-25, 2006.
DOI : 10.1109/AIDM.2006.4
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.8228

D. V. Arnold, Noisy Optimization with Evolution Strategies . Genetic Algorithms and Evolutionary Computation Series, 2002.
DOI : 10.1007/978-1-4615-1105-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.383.9099

. Arnold, . Beyer, D. V. Arnold, and H. Beyer, Local Performance of the (µ/µ i , µ)-ES in a Noisy Environment, Proceedings of the Sixth Workshop on Foundations of Genetic Algorithms, pp.127-141, 2000.

B. Arnold, D. V. Arnold, H. M. Beyer, K. Deb, G. Rudolph et al., Efficiency and Mutation Strength Adaptation of the (??/?? I, ??)-ES in a Noisy Environment, Parallel Problem Solving from Nature PPSN VI: 6th International Conference Proceedings, pp.39-48, 2000.
DOI : 10.1007/3-540-45356-3_4

B. Arnold, D. V. Arnold, and H. Beyer, Investigation of the (??, ??)-ES in the presence of noise, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), pp.332-339, 2001.
DOI : 10.1109/CEC.2001.934409

B. Arnold, D. V. Arnold, and H. Beyer, Local performance of the (1 + 1)-ES in a noisy environment, IEEE Transactions on Evolutionary Computation, vol.6, issue.1, pp.30-41, 2002.
DOI : 10.1109/4235.985690

B. Arnold, D. V. Arnold, and H. Beyer, On the Effects of Outliers on Evolutionary Optimization, Intelligent Data Engineering and Automated Learning: 4th International Conference, pp.151-160, 2003.
DOI : 10.1007/978-3-540-45080-1_22

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.2368

. Astete-morales, Simple and cumulative regret for continuous noisy optimization, Theoretical Computer Science, vol.617, pp.12-27, 2015.
DOI : 10.1016/j.tcs.2015.09.032
URL : https://hal.archives-ouvertes.fr/hal-01194564

. Astete-morales, Evolution Strategies with Additive Noise, Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, pp.76-84, 2015.
DOI : 10.1145/2725494.2725500
URL : https://hal.archives-ouvertes.fr/hal-01077625

. Astete-morales, Analysis of Different Types of Regret in Continuous Noisy Optimization, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, GECCO '16, pp.205-212, 2016.
DOI : 10.1145/2908812.2908933
URL : https://hal.archives-ouvertes.fr/hal-01347814

. Astete-morales, Log-log Convergence for Noisy Optimization, Artificial Evolution -11th International Conference, pp.16-28, 2013.
DOI : 10.1007/978-3-319-11683-9_2
URL : https://hal.archives-ouvertes.fr/hal-01107772

J. Aubin, Viability Theory, 2009.
DOI : 10.1007/978-0-8176-4910-4
URL : https://hal.archives-ouvertes.fr/inria-00636570

P. Auer, Using Confidence Bounds for Exploitation- Exploration Trade-offs, Journal of Machine Learning Research, vol.3, pp.397-422, 2002.

. Auer, Finite time analysis of the multiarmed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

. Auer, Gambling in a rigged casino: The adversarial multi-armed bandit problem, Proceedings of IEEE 36th Annual Foundations of Computer Science, pp.322-331, 1995.
DOI : 10.1109/SFCS.1995.492488

A. Auger, Convergence results for the (1, ? )-SA-ES using the theory of ? -irreducible Markov chains, Theoretical Computer Science, vol.334, pp.1-335, 2005.

A. Auger, Analysis of Comparison-based Stochastic Continuous Black-Box Optimization Algorithms. HabilitationàHabilitation`Habilitationà diriger des recherches en mathématiques et en informatique, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01468781

. Auger, Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1625-1632, 2010.
DOI : 10.1145/1830761.1830782
URL : https://hal.archives-ouvertes.fr/inria-00502441

. Auger, Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noisy BBOB-2010 testbed, Genetic and Evolutionary Computation Conference, GECCO 2010, Proceedings, pp.1611-1616, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00502434

. Auger, Mirrored variants of the (1, 2)-CMA-ES compared on the noisy BBOB-2010 testbed, Genetic and Evolutionary Computation Conference, GECCO 2010, Proceedings, pp.1575-1582, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00502436

H. Auger, A. Auger, and N. Hansen, Linear Convergence on Positively Homogeneous Functions of a Comparison Based Step-Size Adaptive Randomized Search: the (1+1) ES with Generalized One-fifth Success Rule, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00877161

. Auger, Local and global oder 3/2 convergence of a surrogate evolutionary algorithm, Genetic and Evolutionary Computation Conference, GECCO 2005, pp.857-864, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00000540

P. Bach, F. Bach, and V. Perchet, Highly-Smooth Zeroth Order Online Optimization, 29 th Annual Conference on Learning Theory (COLT), pp.257-283, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01321532

R. Bellman, Dynamic Programming, 1957.

. Belloni, Bundle Relaxation and Primal Recovery in Unit Commitment Problems. The Brazilian Case, Annals of Operations Research, vol.120, issue.1/4, pp.21-44, 2003.
DOI : 10.1023/A:1023314026477

J. F. Benders, Partitioning procedures for solving mixed-variables programming problems, Numerische Mathematik, vol.38, issue.1, pp.238-252, 1962.
DOI : 10.1007/BF01386316
URL : http://www.digizeitschriften.de/download/PPN362160546_0004/PPN362160546_0004___log28.pdf

D. P. Bertsekas, Dynamic Programming and Optimal Control, volume I and II, 1995.

D. P. Bertsekas, Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC*, European Journal of Control, vol.11, issue.4-5, pp.310-334, 2005.
DOI : 10.3166/ejc.11.310-334
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.4541

. Bertsimas, Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem, IEEE Transactions on Power Systems, vol.28, issue.1, pp.52-63, 2013.
DOI : 10.1109/TPWRS.2012.2205021

S. Bertsimas, D. Bertsimas, and M. Sim, The Price of Robustness, Operations Research, vol.52, issue.1, pp.35-53, 2004.
DOI : 10.1287/opre.1030.0065

D. Bertsimas and J. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific, 1997.

H. Beyer, ??)-Theory, Evolutionary Computation, vol.15, issue.2, pp.165-188, 1993.
DOI : 10.1162/evco.1993.1.2.165

H. Beyer, Mutate large, but inherit small! On the analysis of rescaled mutations in ( $$\tilde 1,\tilde \lambda$$ )-ES with noisy fitness data, Parallel Problem Solving from Nature ? PPSN V: 5th International Conference Amsterdam Proceedings, pp.109-118, 1998.
DOI : 10.1007/BFb0056854

H. Beyer, The Theory of Evolution Strategies. Natural Computing Series, 2001.

H. Beyer, Actuator Noise in Recombinant Evolution Strategies on General Quadratic Fitness Models, Genetic and Evolutionary Computation ? GECCO 2004: Genetic and Evolutionary Computation Conference Proceedings, Part I, pp.654-665, 2004.
DOI : 10.1007/978-3-540-24854-5_68

H. Beyer and H. Schwefel, Evolution Strategies ? A Comprehensive Introduction, Natural Computing, vol.1, issue.1, pp.3-52, 2002.
DOI : 10.1023/A:1015059928466

J. R. Blum, Approximation Methods which Converge with Probability one, The Annals of Mathematical Statistics, vol.25, issue.2, pp.382-386, 1954.
DOI : 10.1214/aoms/1177728794

J. R. Blum, Multidimensional Stochastic Approximation Methods, The Annals of Mathematical Statistics, vol.25, issue.4, pp.737-744, 1954.
DOI : 10.1214/aoms/1177728659

. Borrett, . Tsang, J. Borrett, and E. P. Tsang, Towards a Formal Framework for Comparing Constraint Satisfaction Problem Formulations, 1996.

J. Branke, C. Schmidt, E. Paz, J. A. Foster, K. Deb et al., Selection in the Presence of Noise, Genetic and Evolutionary Computation ? GECCO 2003: Genetic and Evolutionary Computation Conference Proceedings, Part I, pp.766-777, 2003.
DOI : 10.1007/3-540-45105-6_91

J. Branke, C. Schmidt, X. Yao, E. K. Burke, J. A. Lozano et al., Sequential Sampling in Noisy Environments, Parallel Problem Solving from Nature -PPSN VIII: 8th International Conference. Proceedings, pp.202-211, 2004.
DOI : 10.1007/978-3-540-30217-9_21

. Branke, Efficient fitness estimation in noisy environments, Proceedings of Genetic and Evolutionary Computation, pp.243-250, 2001.

. Bubeck, Pure Exploration in Multi-armed Bandits Problems, Algorithmic Learning Theory, 20th International Conference. Proceedings, pp.23-37, 2009.
DOI : 10.1090/S0002-9904-1952-09620-8
URL : http://arxiv.org/pdf/0802.2655v1.pdf

. 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

D. L. Burkholder, On a Class of Stochastic Approximation Processes, The Annals of Mathematical Statistics, vol.27, issue.4, pp.1044-1059, 1956.
DOI : 10.1214/aoms/1177728072

E. Cantú-paz-]-cantú-paz, Adaptive Sampling for Noisy Problems, Genetic and Evolutionary Computation -GECCO 2004, Genetic and Evolutionary Computation Conference Proceedings , Part I, pp.947-958, 2004.
DOI : 10.1007/978-3-540-24854-5_95

. Cappé, Kullback???Leibler upper confidence bounds for optimal sequential allocation, The Annals of Statistics, vol.41, issue.3, pp.411516-1541, 2013.
DOI : 10.1214/13-AOS1119SUPP

. Castronovo, Benchmarking for Bayesian Reinforcement Learning, PLOS ONE, vol.49, issue.2???3, pp.1-25, 2016.
DOI : 10.1371/journal.pone.0157088.s003
URL : http://doi.org/10.1371/journal.pone.0157088

. Cauwet, A Consistent Model Predictive Control, 2016.

. Cauwet, Algorithm portfolios for noisy optimization, Annals of Mathematics and Artificial Intelligence, vol.11, issue.4, pp.143-172, 2016.
DOI : 10.1007/s10472-015-9486-2
URL : https://hal.archives-ouvertes.fr/hal-01223113

. Cauwet, Algorithm Portfolios for Noisy Optimization: Compare Solvers Early, Learning and Intelligent Optimization: 8th International Conference, pp.1-15, 2014.
DOI : 10.1007/978-3-319-09584-4_1
URL : https://hal.archives-ouvertes.fr/hal-00926638

. Cauwet, Stochastic zero-sum games, 2016.

. Cauwet, . Teytaud, M. Cauwet, and O. Teytaud, Multivariate bias reduction in capacity expansion planning, 2016 Power Systems Computation Conference (PSCC), pp.1-8, 2016.
DOI : 10.1109/PSCC.2016.7540992
URL : https://hal.archives-ouvertes.fr/hal-01306643

. Cauwet, . Teytaud, M. Cauwet, and O. Teytaud, Noisy Optimization, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, GECCO '16, pp.1101-1106, 2016.
DOI : 10.1145/2908812.2908881
URL : https://hal.archives-ouvertes.fr/hal-00844305

. Cauwet, Parallel Evolutionary Algorithms Performing Pairwise Comparisons, Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, pp.99-113, 2015.
DOI : 10.1145/2725494.2725499
URL : https://hal.archives-ouvertes.fr/hal-01077626

K. Chaloner, Bayesian design for estimating the turning point of a quadratic regression, Communications in Statistics - Theory and Methods, vol.72, issue.6, pp.1385-1400, 1989.
DOI : 10.1080/03610928908829973

L. Chapel and G. Deffuant, SVM viability controller active learning, Kernel machines and Reinforcement Learning Workshop, 2006.
DOI : 10.1109/adprl.2007.368188
URL : https://hal.archives-ouvertes.fr/hal-00616861

. Chaslot, Parameter tuning by the cross-entropy method, Proceedings of the 8th European Workshop on Reinforcement Learning, 2008.

. Chatzivasileiadis, The Global Grid, Renewable Energy, vol.57, pp.372-383, 2013.
DOI : 10.1016/j.renene.2013.01.032

. Chaudry, Combined gas and electricity network expansion planning, Applied Energy, vol.113, issue.C, pp.1131171-1187, 2014.
DOI : 10.1016/j.apenergy.2013.08.071

H. Chen, Lower Rate of Convergence for Locating a Maximum of a Function, The Annals of Statistics, vol.16, issue.3, pp.1330-1334, 1988.
DOI : 10.1214/aos/1176350965

G. Cheney, E. W. Cheney, and A. A. Goldstein, Newton's method for convex programming and Tchebycheff approximation, Numerische Mathematik, vol.8, issue.1, pp.253-268, 1959.
DOI : 10.1007/BF01386389
URL : http://www.digizeitschriften.de/download/PPN362160546_0001/PPN362160546_0001___log25.pdf

A. Chotard, Markov chain Analysis of Evolution Strategies . Thesis, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01252128

. Chotard, Cumulative Step-Size Adaptation on Linear Functions, 2012.
DOI : 10.1007/978-3-642-32937-1_8
URL : https://hal.archives-ouvertes.fr/hal-00759577

K. L. Chung, On a Stochastic Approximation Method, The Annals of Mathematical Statistics, vol.25, issue.3, pp.463-483, 1954.
DOI : 10.1214/aoms/1177728716

J. Conca, How Deadly Is Your Kilowatt? We Rank The Killer Energy Sources, 2012.

. Corus, Level-Based Analysis of Genetic Algorithms and Other Search Processes, Parallel Problem Solving from Nature -PPSN XIII, pp.912-921, 2014.
DOI : 10.1007/978-3-319-10762-2_90

A. Couëtoux, Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems. Theses, 2013.

R. Coulom, CLOP: Confident Local Optimization for Noisy??Black-Box Parameter Tuning, Advances in Computer Games, number 7168 in Lecture Notes in Computer Science, pp.146-157, 2011.
DOI : 10.1007/978-3-642-31866-5_13
URL : https://hal.archives-ouvertes.fr/hal-00750326

. Coulom, Handling expensive optimization with large noise, Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, FOGA '11, pp.61-68, 2011.
DOI : 10.1145/1967654.1967660
URL : https://hal.archives-ouvertes.fr/hal-00517157

L. Dang, D. Dang, and P. K. Lehre, Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms, Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, 2015.
DOI : 10.1145/2725494.2725508

G. Dantzig, Linear programming and extensions. Rand Corporation Research Study, 1963.

W. Oliveira and C. Sagastizàbal, BUNDLE METHODS IN THE XXIst CENTURY: A BIRD'S-EYE VIEW, Pesquisa Operacional, vol.34, issue.3, pp.647-670, 2014.
DOI : 10.1590/0101-7438.2014.034.03.0647

J. Decock, Hybridization of dynamic optimization methodologies. Thesis, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01103935

T. Decock, J. Decock, and O. Teytaud, Noisy optimization complexity under locality assumption, Proceedings of the twelfth workshop on Foundations of genetic algorithms XII, FOGA XII '13, pp.183-190, 2013.
DOI : 10.1145/2460239.2460256
URL : https://hal.archives-ouvertes.fr/hal-00755663

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

. Dong, An intervalparameter minimax regret programming approach for power management systems planning under uncertainty, Applied Energy, issue.8, pp.882835-2845, 2011.

V. Dupa?, O Kiefer-Wolfowitzov? aproxima?ní Method?. ? Casopis pro p?stování matematiky, pp.47-75, 1957.

V. Dupa?, Notes on stochastic approximation methods, Czechoslovak Mathematical Journal, vol.08, issue.1, pp.139-149, 1958.

A. Dvoretzky, On Stochastic Approximation, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability Contributions to the Theory of Statistics, pp.39-55, 1956.

B. Efron, The Jackknife, the Bootstrap, and Other Resampling Plans, 1982.
DOI : 10.1137/1.9781611970319

D. Ernst, Se déconnecter du réseaú electrique peutêtre peut?peutêtre rentable, 2015.

D. Ernst, The Global Grid for Empowering Renewable Energy -TEDxLì ege, 2015.

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

[. Fornius and E. , Optimal Design of Experiments for the Quadratic Logistic Model, 2008.

. Finck, . Beyer, S. Finck, and H. Beyer, Benchmarking SPSA on BBOB-2010 noisy function testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1665-1672, 2010.
DOI : 10.1145/1830761.1830787

G. Fitzpatrick, J. M. Fitzpatrick, and J. J. Grefenstette, Genetic algorithms in noisy environments, Machine Learning, pp.101-120, 1988.
DOI : 10.1007/BF00113893
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.543.6967

R. Fonteneau, From Energy To Research ... to Sustainable Energy? http://raphaelfonteneau.blogspot.fr/2014/11/ from-energy-to-research-to-sustainable.html. Accessed, pp.2016-2020, 2015.

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, 2010.
DOI : 10.1007/s00453-010-9391-3
URL : https://hal.archives-ouvertes.fr/inria-00452791

A. Frangioni, Solving Nonlinear Single-Unit Commitment Problems with Ramping Constraints, Operations Research, vol.54, issue.4, pp.767-775, 2006.
DOI : 10.1287/opre.1060.0309
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.384

M. Gagliolo and J. Schmidhuber, A Neural Network Model for Inter-problem Adaptive Online Time Allocation, 2005.

M. Gagliolo and J. Schmidhuber, Learning dynamic algorithm portfolios, Annals of Mathematics and Artificial Intelligence, vol.18, issue.2, pp.295-328, 2006.
DOI : 10.1007/s10472-006-9036-z
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.1145

. Gaudel, A Principled Method for Exploiting Opening Books, Computers and Games, pp.136-144, 2011.
DOI : 10.1007/978-3-642-17928-0_13
URL : https://hal.archives-ouvertes.fr/inria-00484043

L. Gerencsér, Convergence rate of moments in stochastic approximation with simultaneous perturbation gradient approximation and resetting, IEEE Transactions on Automatic Control, vol.44, issue.5, pp.894-905, 1999.
DOI : 10.1109/9.763206

G. Giraud, How Dependent Is Output Growth From Primary Energy? http://www.slideshare.net/PaulineTSP/ lien-entre-le-pib-et-lnergie-par-gal-giraud-ads-20140306, pp.2016-2020, 2015.

O. N. Granichin, Optimal Convergence Rate of the Randomized Algorithms of Stochastic Approximation in Arbitrary Noise, Automation and Remote Control, vol.64, issue.2, pp.252-262, 2003.
DOI : 10.1023/A:1022263014535

K. Grigoriadis, M. D. Grigoriadis, and L. G. Khachiyan, A sublinear-time randomized approximation algorithm for matrix games, Operations Research Letters, vol.18, issue.2, pp.53-58, 1995.
DOI : 10.1016/0167-6377(95)00032-0

Y. Hamadi, Combinatorial Search -From Algorithms to Systems, 2013.
DOI : 10.1007/978-3-642-41482-4

B. Hammel, U. Hammel, and T. Bäck, Evolution strategies on noisy functions how to improve convergence properties, Parallel Problem Solving from Nature ? PPSN III: International Conference on Evolutionary Computation, pp.159-168, 1994.
DOI : 10.1007/3-540-58484-6_260

. Hansen, Evolution Strategies, pp.871-898, 2015.
DOI : 10.1016/j.tcs.2006.04.004
URL : https://hal.archives-ouvertes.fr/hal-01155533

. Hansen, When do heavy-tail distributions help? In Parallel Problem Solving from Nature -PPSN IX, 9th International Conference, pp.62-71, 2006.

. Hansen, 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, pp.180-197, 2009.
DOI : 10.1109/TEVC.2008.924423
URL : https://hal.archives-ouvertes.fr/inria-00276216

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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.7462

R. Hansen, N. Hansen, R. , and R. , Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noisy testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1681-1688, 2010.
DOI : 10.1145/1830761.1830789
URL : https://hal.archives-ouvertes.fr/hal-00545729

J. He and X. Yao, Towards an analytic framework for analysing the computation time of evolutionary algorithms, Artificial Intelligence, vol.145, issue.1-2, pp.59-97, 2003.
DOI : 10.1016/S0004-3702(02)00381-8

. Hedman, A review of transmission switching and network topology optimization, 2011 IEEE Power and Energy Society General Meeting, pp.1-7, 2011.
DOI : 10.1109/PES.2011.6039857

H. Hotelling, Experimental Determination of the Maximum of a Function, The Annals of Mathematical Statistics, vol.12, issue.1, pp.20-45, 1941.
DOI : 10.1214/aoms/1177731784

[. J. Kelley, J. E. Kelley, and J. , The Cutting-Plane Method for Solving Convex Programs, Journal of the Society for Industrial and Applied Mathematics, vol.8, issue.4, pp.703-712, 1960.
DOI : 10.1137/0108053

. Jamieson, Query complexity of derivative-free optimization, Advances in Neural Information Processing Systems 25, pp.2672-2680, 2012.

. Jebalia, Log-Linear Convergence and Divergence of??the??Scale-Invariant (1+1)-ES in Noisy Environments, Algorithmica, vol.13, issue.1, pp.59425-460, 2011.
DOI : 10.1007/s00453-010-9403-3
URL : https://hal.archives-ouvertes.fr/inria-00433347

J. , B. Jin, Y. Branke, and J. , Evolutionary optimization in uncertain environments-a survey, Transacions on Evolutionary Computation, vol.9, issue.3, pp.303-317, 2005.

. Kadioglu, Algorithm Selection and Scheduling, Principles and Practice of Constraint Programming -CP 2011 -17th International Conference, pp.454-469, 2011.
DOI : 10.1007/978-3-642-23786-7_35

N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica, vol.244, issue.S, pp.373-395, 1984.
DOI : 10.1007/BF02579150

. Kezunovic, Reliable Implementation of Robust Adaptive Topology Control, 2014 47th Hawaii International Conference on System Sciences, pp.2493-2502, 2014.
DOI : 10.1109/HICSS.2014.312

. Khuri, Design Issues for Generalized Linear Models: A Review, Statistical Science, vol.21, issue.3, pp.376-399, 2006.
DOI : 10.1214/088342306000000105
URL : http://arxiv.org/abs/math/0701088

. Kiefer, . Wolfowitz, J. Kiefer, and J. Wolfowitz, Stochastic Estimation of the Maximum of a Regression Function, The Annals of Mathematical Statistics, vol.23, issue.3, pp.462-466, 1952.
DOI : 10.1214/aoms/1177729392

. 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

S. Kocsis, L. Kocsis, and C. Szepesvári, Discounted- UCB, pp.784-791, 2006.

. Kormushev, . Caldwell, P. Kormushev, and D. G. Caldwell, Direct policy search reinforcement learning based on particle filtering, The 10th European Workshop on Reinforcement Learning (EWRL 2012), part of the International Conference on Machine Learning, 2012.

L. Kotthoff, Algorithm Selection for Combinatorial Search Problems: A Survey, pp.48-60, 2014.
DOI : 10.1007/978-3-642-31612-8_18

. Kötzing, (1+1) EA on Generalized Dynamic OneMax, Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, pp.40-51, 2015.
DOI : 10.1145/2725494.2725502

R. Lai, T. Lai, and H. Robbins, Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics, vol.6, issue.1, pp.4-22, 1985.
DOI : 10.1016/0196-8858(85)90002-8
URL : http://doi.org/10.1016/0196-8858(85)90002-8

. Latorre, Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1725-1730, 2010.
DOI : 10.1145/1830761.1830795

J. Liu, Portfolio Methods in Uncertain Contexts. Thesis, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01250552

T. Liu, J. Liu, and O. Teytaud, Meta online learning: experiments on a unit commitment problem, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00973397

D. J. Mackay, Sustainable Energy -Without the Hot Air, 2009.

D. Marchal, Les enjeux de la TansitionénergétiqueTansition´Tansitionénergétique pour les smart grids. https://webcache.googleusercontent.com/search? q=cache:XhZL_GuBJmkJ:https://www.inria.fr/content, 2015.

B. L. Miller, Noise, Sampling, and Efficient Genetic Algorithms, 1997.

. Miller, . Goldberg, B. L. Miller, and D. E. Goldberg, Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise, Evolutionary Computation, vol.4, issue.2, pp.113-131, 1996.
DOI : 10.1162/evco.1996.4.2.113

. Mnih, Empirical Bernstein stopping, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.672-679, 2008.
DOI : 10.1145/1390156.1390241
URL : https://hal.archives-ouvertes.fr/hal-00834983

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

. Najafi, . Pourjamal, S. Najafi, and Y. Pourjamal, A New Heuristic Algorithm for Unit Commitment Problem, 2nd International Conference on Advances in Energy Engineering (ICAEE), pp.2005-2011, 2011.
DOI : 10.1016/j.egypro.2011.12.1201

J. Nash, Non-cooperative games, Annals of mathematics, pp.286-295, 1951.
DOI : 10.2307/1969529

J. Nash, Some games and machines for playing them, 1952.

Y. Nesterov, Introductory lectures on convex optimization . A basic course, 2004.
DOI : 10.1007/978-1-4419-8853-9

. Ohlström, Fine-particle emissions of energy production in Finland, Atmospheric Environment, vol.34, issue.22, pp.343701-3711, 2000.
DOI : 10.1016/S1352-2310(00)00076-5

. Ong, Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design, The 2003 Congress on Evolutionary Computation, 2003. CEC '03., pp.1856-1863, 2003.
DOI : 10.1109/CEC.2003.1299898

N. P. Padhy, Unit Commitment???A Bibliographical Survey, IEEE Transactions on Power Systems, vol.19, issue.2, pp.1196-1205, 2004.
DOI : 10.1109/TPWRS.2003.821611

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

P. Pinson, Renewable Energy Forecasts Ought to be Probabilistic ! WIPFOR seminar, 2013.

T. Polyak, B. T. Polyak, and A. B. Tsybakov, Optimal order of accuracy of search algorithms in stochastic optimization, Problems of Information Transmission, vol.26, issue.2, pp.126-133, 1990.

M. J. Powell, The NEWUOA software for unconstrained optimization without derivatives, 2004.
DOI : 10.1007/0-387-30065-1_16

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. B. Powell, Approximate Dynamic Programming : Solving the curse of dimensionality. Wiley series in Probability and Statistics, 2007.

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 : 10.1007/s10601-008-9051-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.8232

M. H. Quenouille, Approximate tests of correlation in time-series 3, Mathematical Proceedings of the Cambridge Philosophical Society, vol.35, issue.03, pp.68-84, 1949.
DOI : 10.1214/aoms/1177731096

L. Raso, Optimal Control of Water Systems Under Forecast Uncertainty: Robust, Proactive, and Integrated, 2013.

I. Rechenberg, Cybernetic solution path of an experimental problem, In Royal Aircraft Establishment Translation No. 1122, B. F. Toms, Trans. Ministry of Aviation, 1965.

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

I. Rechenberg, Evolutionsstrategie '94. Frommann- Holzboog, 1994.

M. Robbins, H. Robbins, and S. Monro, A Stochastic Approximation Method, The Annals of Mathematical Statistics, vol.22, issue.3, pp.400-407, 1951.
DOI : 10.1214/aoms/1177729586

. Rolet, . Teytaud, P. Rolet, O. C. Teytaud, S. Cagnoni et al., Adaptive Noisy Optimization, Applications of Evolutionary Computation, pp.592-601, 2010.
DOI : 10.1007/978-3-642-12239-2_61
URL : https://hal.archives-ouvertes.fr/inria-00459017

. Rolet, . Teytaud, P. Rolet, and O. Teytaud, Bandit-Based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis, Learning and Intelligent Optimization: 4th International Conference, pp.97-110, 2010.
DOI : 10.1007/978-3-642-13800-3_8
URL : https://hal.archives-ouvertes.fr/inria-00437140

R. Ros, Black-box optimization benchmarking the IPOP-CMA-ES on the noisy testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1511-1518, 2010.
DOI : 10.1145/1830761.1830767
URL : https://hal.archives-ouvertes.fr/inria-00473778

R. Ros, Comparison of NEWUOA with different numbers of interpolation points on the BBOB noisy testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1495-1502, 2010.
DOI : 10.1145/1830761.1830765
URL : https://hal.archives-ouvertes.fr/inria-00473776

. Saisirirat, Scenario Analysis of Electric Vehicle Technology Penetration in Thailand: Comparisons of Required Electricity with Power Development Plan and Projections of Fossil Fuel and Greenhouse Gas Reduction, 10th Eco-Energy and Materials Science and Engineering Symposium, pp.459-470, 2013.
DOI : 10.1016/j.egypro.2013.06.774

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

. Saravanan, A solution to the unit commitment problem???a review, Frontiers in Energy, vol.26, issue.1, pp.223-236, 2013.
DOI : 10.1007/s11708-013-0240-3

A. I. Schein and L. H. Ungar, Active learning for logistic regression: an evaluation, Machine Learning, pp.235-265, 2007.
DOI : 10.1007/s10994-007-5019-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.4914

L. Schmetterer, Stochastic Approximation, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability Contributions to the Theory of Statistics, pp.587-609, 1961.

E. Ronald, Neurogenetic truck backer-upper controller, Proceedings of the First IEEE Conference on Evolutionary Computation IEEE World Congress on Computational Intelligence, pp.720-723, 1994.

F. Scholz, The Bootstrap Small Sample Properties, 2007.

A. Schrijver, Theory of Linear and Integer Programming, 1986.

H. Schwefel, Adaptive Mechanismen in der biologischen Evolution, 1974.

O. Shamir, On the complexity of bandit and derivativefree stochastic convex optimization, COLT 2013 -The 26th Annual Conference on Learning Theory, pp.3-24, 2013.

A. Shapiro, Analysis of stochastic dual dynamic programming method, European Journal of Operational Research, vol.209, issue.1, pp.63-72, 2011.
DOI : 10.1016/j.ejor.2010.08.007

. Sheble, G. B. Fahd-]-sheble, and G. N. Fahd, Unit commitment literature synopsis, IEEE Transactions on Power Systems, vol.9, issue.1, pp.128-135, 1994.
DOI : 10.1109/59.317549

. Siqueira, Stochastic Dynamic Programming for Long Term Hydrothermal Scheduling Considering Different Streamflow Models, 2006 International Conference on Probabilistic Methods Applied to Power Systems, pp.1-6, 2006.
DOI : 10.1109/PMAPS.2006.360203

. Solarpower-europe, SolarPower Europe (2015) The succesful stress test of Europe's power grid -more ahead, pp.2016-2020, 2015.

J. C. Spall, A stochastic approximation technique for generating maximum likelihood parameter estimates, Proceedings of the American Control Conference, pp.1161-1167, 1987.

J. C. Spall, Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. Automatic Control, IEEE Transactions on, vol.37, issue.3, pp.332-341, 1992.
DOI : 10.1109/9.119632
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.4562

J. C. Spall, Adaptive stochastic approximation by the simultaneous perturbation method, IEEE Transactions on Automatic Control, issue.10, pp.451839-1853, 2000.

J. C. Spall and J. A. Cristion, Model-free control of nonlinear stochastic systems with discrete-time measurements, IEEE Transactions on Automatic Control, vol.43, issue.9, pp.1198-1210, 1998.
DOI : 10.1109/9.718605

L. St-pierre, D. L. St-pierre, and J. Liu, Differential Evolution algorithm applied to non-stationary bandit problem, 2014 IEEE Congress on Evolutionary Computation (CEC), pp.2397-2403, 2014.
DOI : 10.1109/CEC.2014.6900505
URL : https://hal.archives-ouvertes.fr/hal-00979456

T. St-pierre, D. L. St-pierre, and O. Teytaud, The Nash and the bandit approaches for adversarial portfolios, 2014 IEEE Conference on Computational Intelligence and Games, pp.1-7, 2014.
DOI : 10.1109/CIG.2014.6932897

D. I. Stern and K. Enflo, Causality between energy and output in the long-run, Energy Economics, vol.39, pp.135-146, 2013.
DOI : 10.1016/j.eneco.2013.05.007

P. Storn, R. Storn, and K. Price, Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, CA, 1995.

B. Sutton, R. S. Sutton, and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

[. Workgroup, European, CIS and Mediterranean Interconnection: State of Play, 2006.

O. Teytaud, When Does Quasi-random Work?, Parallel Problem Solving from Nature ? PPSN X: 10th International Conference Proceedings, pp.325-336, 2008.
DOI : 10.1007/978-3-540-87700-4_33
URL : https://hal.archives-ouvertes.fr/inria-00287863

. Teytaud, Algorithms (X,sigma,eta) : quasi-random mutations for Evolution Strategies, Evolution Artificielle, pp.296-307, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00000544

T. Tran and G. Jin, Benchmarking real-coded genetic algorithm on noisy black-box optimization testbed, Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, GECCO '10, pp.1739-1744, 2010.
DOI : 10.1145/1830761.1830797

P. E. Utgoff, Perceptron Trees: A Case Study in Hybrid Concept Representations, Connection Science, vol.4, issue.4, pp.377-391, 1989.
DOI : 10.1023/A:1022699900025

W. Vaart, A. V. Vaart, and J. Wellner, Weak Convergence and Empirical Processes, 1996.

V. Vapnik and A. Chervonenkis, On the uniform convergence of frequencies of occurence events to their probabilities, Soviet Mathematics-Doklady, vol.9, pp.915-918, 1968.

V. N. Vapnik, The Nature of Statistical Learning, 1995.

. Vassilevska, Confronting hardness using a hybrid approach, Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm , SODA '06, pp.1-10, 2006.
DOI : 10.1145/1109557.1109558

. Virmani, Implementation of a Lagrangian relaxation based unit commitment problem, IEEE Transactions on Power Systems, vol.4, issue.4, pp.1373-1380, 1989.
DOI : 10.1109/59.41687

B. Von-stengel, Computing equilibria for two-person games. Handbook of Game Theory, pp.1723-1759, 2002.

J. Wellner, Bootstrap and Jackknife Estimation of Sampling Distributions, 2014.

D. Whitley, Mk Landscapes, NK Landscapes, MAXkSAT: A Proof That the Only Challenging Problems Are Deceptive, Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation , GECCO '15, pp.927-934, 2015.

. Whitmarsh, Gas security of supply report, 2012.

D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.67-82, 1997.
DOI : 10.1109/4235.585893
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.6606

. Hydra-mip, Automated algorithm configuration and selection for mixed integer programming, RCRA workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion at the International Joint Conference on Artificial Intelligence (IJCAI), pp.16-30

B. Yu, Rates of Convergence for Empirical Processes of Stationary Mixing Sequences, The Annals of Probability, vol.22, issue.1, pp.94-116, 1994.
DOI : 10.1214/aop/1176988849

. 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.22, issue.6, pp.598-609, 2011.
DOI : 10.1590/S0103-17592011000600005

. Zaoui, Coupling OPF and topology optimization for security purposes, 15 th Power Systems Computation Conference, pp.22-26, 2005.

M. Zedek, Continuity and location of zeros of linear combinations of polynomials, Proceedings of the American Mathematical Society, pp.78-84, 1965.
DOI : 10.1090/S0002-9939-1965-0171902-8

X. Zhan, Extremal Eigenvalues of Real Symmetric Matrices with Entries in an Interval, SIAM Journal on Matrix Analysis and Applications, vol.27, issue.3, pp.851-860, 2005.
DOI : 10.1137/050627812

. Zhou, Hierarchical surrogate-assisted evolutionary optimization framework, IEEE Congress on Evolutionary Computation, pp.1586-1593, 2004.