A. Auger, Convergence results for the <mml:math altimg="si1.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>??</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>-SA-ES using the theory of <mml:math altimg="si2.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mi>??</mml:mi></mml:math>-irreducible Markov chains, Theoretical Computer Science, vol.334, issue.1-3, pp.35-69, 2005.
DOI : 10.1016/j.tcs.2004.11.017

A. Auger, M. Jebalia, and O. Teytaud, Xse: quasi-random mutations for evolution strategies, Proceedings of Evolutionary Algorithms, 2005.

R. Bellman, Dynamic Programming, 1957.

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

S. Christensen and F. Oppacher, What can we learn from no free lunch? a first attempt to characterize the concept of a searchable function, Proceedings of the Genetic and Evolutionary Computation Conference, pp.1219-1226, 2001.

S. Droste, Not all linear functions are equally difficult for the compact genetic algorithm, Proceedings of the 2005 conference on Genetic and evolutionary computation , GECCO '05, pp.679-686, 2005.
DOI : 10.1145/1068009.1068124

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

C. Gagné and M. Parizeau, Open BEAGLE, Late breaking papers of the GECCO 2002, pp.161-168, 2002.
DOI : 10.1145/1138470.1138473

S. Gelly, O. Teytaud, and C. Gagné, Resource-Aware Parameterizations of EDA, 2006 IEEE International Conference on Evolutionary Computation, 2006.
DOI : 10.1109/CEC.2006.1688620
URL : https://hal.archives-ouvertes.fr/inria-00112803

D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, 1989.

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2003.
DOI : 10.1016/0004-3702(95)00124-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.7462

R. Herbrich, T. Graepel, and C. Campbell, Bayes point machines, Journal of Machine Learning Research, vol.1, pp.245-279, 2001.

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

J. Jagerskupper and C. Witt, Runtime analysis of a (mu+1)es for the sphere function, 2005.

A. F. Kaupe, Algorithm 178: direct search, Communications of the ACM, vol.6, issue.6, pp.313-314, 1963.
DOI : 10.1145/366604.366632

J. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-311, 1965.
DOI : 10.1093/comjnl/7.4.308

Y. Nikulin, Robustness in combinatorial optimization and scheduling theory: An annotated bibliography. Optimization online, 2004.

G. Rudolph, Convergence rates of evolutionary algorithms for a class of convex objective functions, Control and Cybernetics, vol.26, issue.3, pp.375-390, 1997.

Y. Semet and M. Schoenauer, On the benefits of inoculation, an example in train scheduling, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.1761-1768, 2006.
DOI : 10.1145/1143997.1144283
URL : https://hal.archives-ouvertes.fr/inria-00116345

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

O. Teytaud and S. Gelly, General Lower Bounds for Evolutionary Algorithms, 10 th International Conference on Parallel Problem Solving from Nature, 2006.
DOI : 10.1007/11844297_3
URL : https://hal.archives-ouvertes.fr/inria-00112820

V. Torczon, On the Convergence of the Multidirectional Search Algorithm, SIAM Journal on Optimization, vol.1, issue.1, pp.123-145, 1991.
DOI : 10.1137/0801010

. Villemonteix, An informational approach to the global optimization of expensive-to-evaluate functions, Journal of Global Optimization, vol.10, issue.5
DOI : 10.1007/s10898-008-9354-2
URL : https://hal.archives-ouvertes.fr/hal-00354262

L. D. Whitley, K. Bush, R. , and J. E. , Subthreshold-Seeking Behavior and Robust Local Search, Proceedings of GECCO, pp.282-293, 2004.
DOI : 10.1007/978-3-540-24855-2_25

D. Wolpert and W. Macready, No free lunch theorems for search, 1995.

M. Wright, Direct search methods: Once scorned, now respectable Numerical Analysis, Pitman Research Notes in Mathematics, pp.191-208, 1995.

C. Zhu, R. Byrd, P. Lu, and J. Nocedal, L-BFGS-B: a limited memory FORTRAN code for solving bound constrained optimization problems, 1994.