B. Adenso-díaz and M. Laguna, Fine-tuning of algorithms using Fractional Experimental Design and Local Search, Operations Research, vol.54, issue.1, pp.99-114, 2006.

C. Ansótegui, M. Sellmann, and K. Tierney, A gender-based genetic algorithm for the automatic configuration of algorithms, Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, CP'09, pp.142-157, 2009.

N. Belkhir, J. Dreo, P. Savant, and M. Schoenauer, Feature based algorithm configuration: A case study with differential evolution, PPSN XIV, vol.9921, pp.156-165, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01359539

A. Borghetti, C. Ambrosio, A. Lodi, and S. Martello, An MILP approach for short-term hydro scheduling and unit commitment with head-dependent reservoir, IEEE Transactions on Power Systems, vol.23, issue.3, pp.1115-1124, 2008.

M. Brendel and M. Schoenauer, Instance-based parameter tuning for Evolutionary AI Planning, Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO '11, pp.591-598, 2011.

N. Cristianini and J. Shawe-taylor, An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods, 2000.

K. Eggensperger, M. Lindauer, and F. Hutter, Pitfalls and best practices in algorithm configuration, 2017.

F. Hutter, H. H. Hoos, K. Leyton-brown, and T. Stützle, ParamILS: An automatic algorithm configuration framework, J. Artif. Int. Res, vol.36, issue.1, pp.267-306, 2009.

F. Hutter and H. Youssef, Parameter adjustment based on performance prediction: Towards an instance-aware problem solver, 2005.

, IBM. IBM ILOG CPLEX Optimization Studio CPLEX Parameters Reference, 2014.

M. Lombardi, M. Milano, and A. Bartolini, Empirical decision model learning, Artif. Intell, vol.244, pp.343-367, 2017.

M. López-ibáñez, J. Dubois-lacoste, L. Cáceres, M. Birattari, and T. Stützle, The irace package: iterated racing for automatic algorithm configuration, Operations Research Perspectives, vol.3, pp.43-58, 2016.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2012.

V. Nannen and A. E. Eiben, Relevance estimation and value calibration of evolutionary algorithm parameters, Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI'07, pp.975-980, 2007.

S. Shalev-shwartz and S. Ben-david, Understanding Machine Learning: From Theory to Algorithms, 2014.

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.

S. Varma and R. Simon, Bias in error estimation when using cross-validation for model selection, BMC bioinformatics, vol.7, issue.91, 2006.