C. Ansótegui, Y. Malitsky, H. Samulowitz, M. Sellmann, and K. Tierney, Model-Based Genetic Algorithms for Algorithm Configuration, Proc. of International Conf. on Artificial Intelligence (IJCAI'15). AAAI, pp.733-739, 2015.

T. Bartz-beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Experimental Methods for the Analysis of Optimization Algorithms, 2010.

T. Bartz-beielstein, O. Flasch, P. Koch, and W. Konen, SPOT: A Toolbox for Interactive and Automatic Tuning in the R Environment, Proc. of the 20th Workshop Computational Intelligence. Universitätsverlag Karlsruhe, pp.264-273, 2010.

N. Belkhir, J. Dréo, P. Savéant, and M. Schoenauer, Feature Based Algorithm Configuration: A Case Study with Differential Evolution, Proc. of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV) (Lecture Notes in Computer Science, vol.9921, pp.156-166, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01359539

N. Belkhir, J. Dreo, P. Savéant, and M. Schoenauer, Surrogate Assisted Feature Computation for Continuous Problems, Proc. of Learning and Intelligent Optimization (LION'16), vol.10079, pp.17-31, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01303320

N. Belkhir, J. Dréo, P. Savéant, and M. Schoenauer, Per Instance Algorithm Configuration of CMA-ES with Limited Budget, Proc. of the 19th Annual Conference on Genetic and Evolutionary Computation (GECCO), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01613753

, , pp.681-688

J. Bossek, smoof: Single-and Multi-Objective Optimization Test Functions, The R Journal, 2017.

E. K. Burke, M. Gendreau, M. Hyde, G. Kendall, G. Ochoa et al., Hyper-Heuristics: A Survey of the State of the Art, Journal of the Operational Research Society, vol.64, pp.1695-1724, 2013.

N. Dang and C. Doerr, Hyper-Parameter Tuning for the (1 + (?, ?)) GA, Proc. of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO'19), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02175766

L. Devroye, The Compound Random Search, Ph.D. Dissertation. Purdue University, 1972.

L. Dixon, The Choice of Step Length, a Crucial Factor in the Performance of Variable Metric Algorithms, Numerical Methods for Non-Linear Optimization, pp.149-170, 1972.

B. Doerr and C. Doerr, Optimal Static and Self-Adjusting Parameter Choices for the (1 + (?, ?)) Genetic Algorithm, Algorithmica, vol.80, pp.1658-1709, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01668262

B. Doerr and C. Doerr, Theory of Parameter Control Mechanisms for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices, Theory of Randomized Search Heuristics in Discrete Search Spaces, 2018.

B. Doerr, C. Doerr, and F. Ebel, From Black-Box Complexity to Designing New Genetic Algorithms, Theoretical Computer Science, vol.567, pp.87-104, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01272858

B. Doerr, C. Doerr, and J. Lengler, Self-Adjusting Mutation Rates with Provably Optimal Success Rules, Proc. of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO'19), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02175768

B. Doerr, T. Jansen, D. Sudholt, C. Winzen, and C. Zarges, Mutation Rate Matters Even When Optimizing Monotonic Functions, Evolutionary Computation, vol.21, pp.1-27, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01086549

C. Doerr and M. Wagner, On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems, Proc. of the 20th Annual Conference on Genetic and Evolutionary Computation (GECCO'18), pp.943-950, 2018.

C. Doerr and M. Wagner, Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization, International Conference on Parallel Problem Solving from Nature (PPSN'18), vol.11102, pp.360-372, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01921055

M. Feurer and F. Hutter, Hyperparameter Optimization, Automated Machine Learning: Methods, Systems, Challenges, pp.3-38, 2019.

J. J. Grefenstette, Optimization of Control Parameters for Genetic Algorithms, IEEE Trans. on Systems, Man, and Cybernetics, vol.16, pp.122-128, 1986.

N. Hansen, A. Auger, O. Mersmann, T. Tu?ar, and D. Brockhoff, COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294124

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

C. Hanster and P. Kerschke, flaccogui: Exploratory Landscape Analysis for Everyone, Proc. of the Genetic and Evolutionary Computation Conference Companion (GECCO'17), pp.1215-1222, 2017.

F. Hutter, Y. Hamadi, H. Holger, K. Hoos, and . Leyton-brown, Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms, International Conference on Principles and Practice of Constraint Programming, pp.213-228, 2006.

F. Hutter, H. Holger, K. Hoos, and . Leyton-brown, Sequential Model-Based Optimization for General Algorithm Configuration, Proc. of Learning and Intelligent Optimization (LION'11), pp.507-523, 2011.

G. Karafotias, M. Hoogendoorn, and Á. E. Eiben, Parameter Control in Evolutionary Algorithms: Trends and Challenges, IEEE Transactions on Evolutionary Computation, vol.19, pp.167-187, 2015.

P. Kerschke, H. Holger, F. Hoos, H. Neumann, and . Trautmann, Automated Algorithm Selection: Survey and Perspectives, Evolutionary Computation, vol.27, pp.3-45, 2019.

P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J. Sun et al., Cell Mapping Techniques for Exploratory Landscape Analysis, EVOLVE -A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, pp.115-131, 2014.

P. Kerschke, M. Preuss, S. Wessing, and H. Trautmann, Detecting Funnel Structures by Means of Exploratory Landscape Analysis, Proc. of the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO'15), pp.265-272, 2015.

P. Kerschke, M. Preuss, S. Wessing, and H. Trautmann, Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models, Proc. of the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO'16), 2016.

P. Kerschke and H. Trautmann, Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning, Evolutionary Computation, vol.27, pp.99-127, 2019.

J. Lengler, A General Dichotomy of Evolutionary Algorithms on Monotone Functions, International Conference on Parallel Problem Solving from Nature (PPSN'18), vol.11102, pp.3-15, 2018.

K. Leyton-brown, E. Nudelman, and Y. Shoham, Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions, Principles and Practice of Constraint Programming-CP 2002, pp.556-572, 2002.

L. Li, K. G. Jamieson, and G. Desalvo, Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, Journal of Machine Learning Research, vol.18, 2017.

F. G. Lobo and C. F. Lima, 2007. Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence, vol.54

M. López-ibáñez, J. Dubois-lacoste, L. P. Cáceres, M. Birattari, and T. Stützle, The irace package: Iterated Racing for Automatic Algorithm Configuration, Operations Research Perspectives, vol.3, pp.43-58, 2016.

O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs et al., Exploratory Landscape Analysis, Proc. of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO'11), pp.829-836, 2011.

, Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space, Evolutionary Computation (ECJ), vol.25, pp.529-554, 2017.

Y. Pushak, H. Holger, and . Hoos, Algorithm Configuration Landscapes: -More Benign Than Expected, International Conference on Parallel Problem Solving from Nature (PPSN'18), vol.11102, pp.271-283, 2018.

C. François-michel-de-rainville, O. Gagné, D. Teytaud, and . Laurendeau, Evolutionary Optimization of Low-Discrepancy Sequences, ACM Transactions on Modeling and Computer Simulation, vol.22, pp.1-9, 2012.

J. Rapin, M. Gallagher, P. Kerschke, M. Preuss, and O. Teytaud, Exploring the MLDA Benchmark on the Nevergrad Platform, Proc. of the 21st Annual Conference on Genetic and Evolutionary Computation, 2019.

J. Rapin and O. Teytaud, Nevergrad -A Gradient-Free Optimization Platform, 2018.

, Ingo Rechenberg. 1973. Evolutionsstrategie. Friedrich Fromman Verlag (Günther Holzboog KG)

A. Michael, K. Schumer, and . Steiglitz, Adaptive Step Size Random Search, IEEE Transactions on Automatic Control, vol.13, pp.270-276, 1968.

H. Sander-van-rijn, . Wang, T. Matthijs-van-leeuwen, and . Bäck, Evolving the Structure of Evolution Strategies, Proc. of IEEE Symposium Series on Computational Intelligence (SSCI'16), 2016.