P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multi-armed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

H. J. Barbosa and A. M. Sá, On adaptive operator probabilities in real coded genetic algorithms, In: XX Intl. Conference of the Chilean Computer Science Society, 2000.

T. Bartz-beielstein, C. Lasarczyk, and M. Preuss, Sequential Parameter Optimization, 2005 IEEE Congress on Evolutionary Computation, pp.773-780, 2005.
DOI : 10.1109/CEC.2005.1554761

M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp, A racing algorithm for configuring metaheuristics, Proc. Genetic and Evolutionary Computation Conference, pp.11-18, 2002.

P. Collet and M. Schoenauer, GUIDE: Unifying Evolutionary Engines through a Graphical User Interface, Proc. Intl. Conference on Artificial Evolution, pp.203-215, 2003.
DOI : 10.1007/978-3-540-24621-3_17

URL : https://hal.archives-ouvertes.fr/inria-00201074

W. J. Conover, Practical Nonparametric Statistics, 1999.

D. Costa, L. Fialho, A. Schoenauer, M. Sebag, and M. , Adaptive operator selection with dynamic multi-armed bandits, Proc. Genetic and Evolutionary Computation Conference, pp.913-920, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00278542

L. Davis, Adapting operator probabilities in genetic algorithms, Proc. Intl. Conference on Genetic Algorithms, pp.61-69, 1989.

K. Dejong, Parameter setting in EAs: a 30 year perspective, pp.1-18

A. E. Eiben, R. Hinterding, and Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.3, issue.2, pp.124-141, 1999.
DOI : 10.1109/4235.771166

URL : https://hal.archives-ouvertes.fr/inria-00140549

A. E. Eiben, Z. Michalewicz, M. Schoenauer, J. E. Smith, and . Lobo, Parameter control in evolutionary algorithms, pp.19-46
URL : https://hal.archives-ouvertes.fr/inria-00140549

A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, 2003.

A. Fialho, L. Da-costa, M. Schoenauer, and M. Sebag, Extreme Value Based Adaptive Operator Selection, Proc. Intl. Conference on Parallel Solving from Nature, pp.175-184, 2008.
DOI : 10.1007/978-3-540-87700-4_18

URL : https://hal.archives-ouvertes.fr/inria-00287355

A. Fialho, L. Da-costa, M. Schoenauer, and M. Sebag, Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms, pp.176-190
DOI : 10.1007/978-3-642-11169-3_13

URL : https://hal.archives-ouvertes.fr/inria-00377401

A. Fialho, M. Schoenauer, and M. Sebag, Analysis of adaptive operator selection techniques on the royal road and long k-path problems, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pp.779-786, 2009.
DOI : 10.1145/1569901.1570009

URL : https://hal.archives-ouvertes.fr/inria-00377449

D. B. Fogel, Phenotypes, genotypes, and operators in evolutionary computation, Proceedings of 1995 IEEE International Conference on Evolutionary Computation, 1995.
DOI : 10.1109/ICEC.1995.489143

M. Gagliolo and J. Schmidhuber, Algorithm selection as a bandit problem with unbounded losses, 2008.

D. Goldberg, Probability matching, the magnitude of reinforcement, and classifier system bidding, Machine Learning, vol.2, issue.4, pp.407-426, 1990.
DOI : 10.1007/BF00116878

S. Gould and N. Eldredge, Punctuated equilibria: the tempo and mode of evolution reconsidered, Paleobiology, vol.31, issue.02, pp.115-151, 1977.
DOI : 10.2307/2407283

C. Hartland, N. Baskiotis, S. Gelly, O. Teytaud, and M. Sebag, Change point detection and meta-bandits for online learning in dynamic environments, Proc. Conférence Francophone sur l'Apprentissage Automatique, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00164033

C. Hartland, S. Gelly, N. Baskiotis, O. Teytaud, and M. Sebag, Multi-armed bandit, dynamic environments and meta-bandits, Online Trading of Exploration and Exploitation Workshop, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00113668

D. Hinkley, Inference about the change-point from cumulative sum tests, Biometrika, vol.58, issue.3, pp.509-523, 1970.
DOI : 10.1093/biomet/58.3.509

J. H. Holland, Royal road functions, In: Internet Genetic Algorithms Digest Massachusetts Institute of Technology, vol.7, p.22, 1993.

T. Jones, A Description of Holland's Royal Road Function, Evolutionary Computation, vol.7, issue.4, pp.409-415, 1994.
DOI : 10.1162/evco.1994.2.4.409

B. Julstrom, What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm, Proc. Intl. Conference on Genetic Algorithms, pp.81-87, 1995.

L. Kallel and M. Schoenauer, Fitness distance correlation for variable length representations, Tech. Rep, vol.363, 1996.

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

F. Lobo and D. Goldberg, Decision making in a hybrid genetic algorithm, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), pp.121-125, 1997.
DOI : 10.1109/ICEC.1997.592281

F. Lobo, C. Lima, and Z. Michalewicz, Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence, vol.54, 2007.
DOI : 10.1007/978-3-540-69432-8

J. Maturana, A. Fialho, F. Saubion, M. Schoenauer, and M. Sebag, Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection, 2009 IEEE Congress on Evolutionary Computation, pp.365-372, 2009.
DOI : 10.1109/CEC.2009.4982970

URL : https://hal.archives-ouvertes.fr/inria-00377450

J. Maturana, F. Lardeux, and F. Saubion, Autonomous operator management for evolutionary algorithms, Journal of Heuristics, vol.32, issue.8, 2010.
DOI : 10.1007/s10732-010-9125-3

J. Maturana and F. Saubion, A Compass to Guide Genetic Algorithms, Proc. Intl. Conference on Parallel Solving from Nature, pp.256-265, 2008.
DOI : 10.1007/978-3-540-87700-4_26

Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3 edn, 1996.

M. Mitchell, S. Forrest, and J. H. Holland, The royal road for genetic algorithms: Fitness landscapes and GA performance, Proc. European Conference on Artificial Life, pp.245-254, 1992.

V. Nannen and A. E. Eiben, Efficient relevance estimation and value calibration of evolutionary algorithm parameters, 2007 IEEE Congress on Evolutionary Computation, pp.975-980, 2007.
DOI : 10.1109/CEC.2007.4424460

R. J. Quick, V. J. Rayward-smith, and G. D. Smith, The Royal Road functions: description, intent and experimentation, Selected Papers from AISB Workshop on Evolutionary Computing, pp.223-235, 1996.
DOI : 10.1007/BFb0032786

W. Spears, Adapting crossover in evolutionary algorithms, Proc. Conference on Evolutionary Programming, pp.367-384, 1995.

D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, Proceedings of the 2005 conference on Genetic and evolutionary computation , GECCO '05, pp.1539-1546, 2005.
DOI : 10.1145/1068009.1068251

D. Thierens and . Lobo, Adaptive Strategies for Operator Allocation, pp.77-90
DOI : 10.1007/978-3-540-69432-8_4

A. Tuson and P. Ross, Adapting Operator Settings in Genetic Algorithms, Evolutionary Computation, vol.1, issue.3, pp.161-184, 1998.
DOI : 10.1162/evco.1998.6.2.161

J. Whitacre, T. Pham, and R. Sarker, Use of statistical outlier detection method in adaptive evolutionary algorithms, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.1345-1352, 2006.
DOI : 10.1145/1143997.1144205

T. Yu, D. Davis, C. Baydar, and R. Roy, Evolutionary Computation in Practice, Studies in Computational Intelligence, vol.88, 2008.
DOI : 10.1007/978-3-540-75771-9

B. Yuan and M. Gallagher, Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms, Proc. Intl. Conference on Parallel Solving from Nature, pp.172-181, 2004.
DOI : 10.1007/978-3-540-30217-9_18