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

H. Schwefel, Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie (Birkhäuser, 1977.

H. Beyer and H. , Schwefel, Natural computing, vol.1, issue.1, pp.3-52, 2002.
DOI : 10.1007/978-3-642-99669-6_19

H. Beyer, The Theory of Evolution Strategies, 2001.
DOI : 10.1007/978-3-662-04378-3

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, pp.159-195, 2001.
DOI : 10.1016/0004-3702(95)00124-7

H. Schwefel and G. Rudolph, Contemporary evolution strategies Advances in Artificial Life, pp.891-907, 1995.

G. Rudolph, Convergence Properties of Evolutionary Algorithms, 1997.

D. V. Arnold, Weighted multirecombination evolution strategies, Theoretical Computer Science, vol.361, issue.1, pp.18-37, 2006.
DOI : 10.1016/j.tcs.2006.04.003

H. Mühlenbein and D. , Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization, Evolutionary Computation, vol.9, issue.1, pp.25-49, 1993.
DOI : 10.1287/moor.6.1.19

H. Beyer, Toward a Theory of Evolution Strategies: On the Benefits of Sex??? the (??/??, ??) Theory, Evolutionary Computation, vol.3, issue.1, pp.81-111, 1995.
DOI : 10.1162/evco.1993.1.2.165

C. Kappler, Are evolutionary algorithms improved by large mutations?, Parallel Problem Solving from, Nature

G. Rudolph, Local convergence rates of simple evolutionary algorithms with Cauchy mutations, IEEE Transactions on Evolutionary Computation, vol.1, issue.4, pp.249-258, 1997.
DOI : 10.1109/4235.687885

X. Yao, Y. Liu, and G. Lin, Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, vol.3, issue.2, pp.82-102, 1999.

M. Herdy, The number of offspring as strategy parameter in hierarchically organized evolution strategies, ACM SIGBIO Newsletter, vol.13, issue.2, pp.2-9, 1993.
DOI : 10.1145/163428.163431

M. Schumer and K. Steiglitz, Adaptive step size random search, IEEE Transactions on Automatic Control, vol.13, issue.3, pp.270-276, 1968.
DOI : 10.1109/TAC.1968.1098903

S. Kern, S. D. Müller, N. Hansen, D. Büche, J. Ocenasek et al., Learning probability distributions in continuous evolutionary algorithms ??? a comparative review, Natural Computing, vol.3, issue.1, pp.77-112, 2004.
DOI : 10.1023/B:NACO.0000023416.59689.4e

N. Hansen, An Analysis of Mutative ??-Self-Adaptation on Linear Fitness Functions, Evolutionary Computation, vol.14, issue.3, pp.255-275, 2006.
DOI : 10.1109/4235.771163

A. Ostermeier, A. Gawelczyk, and N. Hansen, A Derandomized Approach to Self-Adaptation of Evolution Strategies, Evolutionary Computation, vol.2, issue.4, pp.369-380, 1994.
DOI : 10.1162/evco.1994.2.4.369

T. Runarsson, Reducing random fluctuations in mutative self-adaptation, Parallel Problem Solving from Nature (PPSN VII), pp.194-203, 2002.

A. Ostermeier, A. Gawelczyk, and N. Hansen, Stepsize adaptation based on non-local use of selection information, Parallel Problem Solving from, Nature, pp.189-198, 1994.

N. Hansen, A. Ostermeier, and A. Gawelczyk, On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation, International Conference on Genetic Algorithms (ICGA '95), pp.57-64, 1995.

N. Hansen and A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, Proceedings of IEEE International Conference on Evolutionary Computation, pp.312-317, 1996.
DOI : 10.1109/ICEC.1996.542381

A. Ostermeier and N. Hansen, An evolution strategy with coordinate system invariant adaptation of arbitrary normal mutation distributions within the concept of mutative strategy parameter control, Genetic and Evolutionary Computation Conference, pp.902-909, 1999.

D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber, Natural Evolution Strategies, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.3381-3387, 2008.
DOI : 10.1109/CEC.2008.4631255

Y. Sun, D. Wierstra, T. Schaul, and J. , Schmidhuber: Efficient natural evolution strategies, Genetic and Evolutionary Computation Conference, pp.539-546, 2009.

T. Glasmachers, T. Schaul, Y. Sun, D. Wierstra, and J. Schmidhuber, Exponential natural evolution strategies, Genetic and Evolutionary Computation Conference, pp.393-400, 2010.

A. Ostermeier, An evolution strategy with momentum adaptation of the random number distribution , Parallel Problem Solving from Nature (PPSN II), pp.199-208, 1992.

J. Poland and A. , Zell: Main vector adaptation: A CMA variant with linear time and space complexity , Genetic and Evolutionary Computation Conference, pp.1050-1055, 2001.

J. N. Knight and M. Lunacek, Reducing the space-time complexity of the CMA-ES, Genetic and Evolutionary Computation Conference, pp.658-665, 2007.

N. Hansen and S. Kern-yao, Evaluating the CMA evolution strategy on multimodal test functions, Parallel Problem Solving from Nature (PPSN VIII), pp.282-291, 2004.

G. A. Jastrebski and D. V. Arnold, Improving Evolution Strategies through Active Covariance Matrix Adaptation, 2006 IEEE International Conference on Evolutionary Computation, pp.2814-2821, 2006.
DOI : 10.1109/CEC.2006.1688662

H. Beyer, Mutate large, but inherit small! On the analysis of rescaled mutations in

Y. Sun, D. Wierstra, T. Schaul, and J. Schmidhuber, Stochastic search using the natural gradient, International Conference on Machine Learning (ICML '09), pp.1161-1168, 2009.

Y. Akimoto, Y. Nagata, I. Ono, and S. Kobayashi, Bidirectional relation between CMA evolution strategies and natural evolution strategies, Parallel Problem Solving from, Nature, pp.154-163, 2010.

L. Arnold, A. Auger, N. Hansen, and Y. , Ollivier: Information-geometric optimization algorithms: A unifying picture via invariance principles, pp.ArXiv e-prints, 2011.

M. Pelikan, M. W. Hausschild, and F. G. Lobo, Introduction to estimation of distribution algorithms, Handbook of Computational Intelligence, 2013.

G. R. Harik and F. G. , Lobo: A parameter-less genetic algorithm, Genetic and Evolutionary Computation Conference, Banzhaf et al, pp.258-265, 1999.

F. G. Lobo and D. E. Goldberg, The parameter-less genetic algorithm in practice, Information Sciences, vol.167, issue.1-4, pp.217-232, 2004.
DOI : 10.1016/j.ins.2003.03.029

A. Auger and N. , Hansen: A restart CMA evolution strategy with increasing population size, IEEE Congress on Evolutionary Computation, pp.1769-1776, 2005.

T. Suttorp, N. Hansen, and C. , Efficient covariance matrix update for variable metric evolution strategies, Machine Learning, vol.7, issue.2, pp.167-197, 2009.
DOI : 10.1007/s10994-009-5102-1
URL : https://hal.archives-ouvertes.fr/inria-00369468

Z. Michalewicz and M. Schoenauer, Evolutionary Algorithms for Constrained Parameter Optimization Problems, Evolutionary Computation, vol.13, issue.1, pp.1-32, 1996.
DOI : 10.1162/evco.1996.4.1.1

E. Mezura-montes and C. A. , Constraint-handling in nature-inspired numerical optimization: Past, present and future, Swarm and Evolutionary Computation, vol.1, issue.4, pp.173-194, 2011.
DOI : 10.1016/j.swevo.2011.10.001

M. Emmerich, A. Giotis, M. Ozdemir, T. Bäck, and K. , Giannakoglou: Metamodel-assisted evolution strategies, Parallel Problem Solving from, Nature, pp.361-370, 2002.

C. Igel, N. Hansen, and S. Roth, Covariance Matrix Adaptation for Multi-objective Optimization, Evolutionary Computation, vol.15, issue.1, pp.1-28, 2007.
DOI : 10.1109/TEVC.2003.810758

N. Hansen, T. Voß, and C. , Igel: Improved step size adaptation for the MO-CMA-ES, Genetic and Evolutionary Computation Conference, pp.487-494, 2010.

R. Salomon, Evolutionary algorithms and gradient search: similarities and differences, IEEE Transactions on Evolutionary Computation, vol.2, issue.2, pp.45-55, 1998.
DOI : 10.1109/4235.728207

G. Rudolph, Self-adaptive mutations may lead to premature convergence, IEEE Transactions on Evolutionary Computation, vol.5, issue.4, pp.410-414, 2001.
DOI : 10.1109/4235.942534

A. Auger and N. Hansen, Reconsidering the progress rate theory for evolution strategies in finite dimensions, Genetic and Evolutionary Computation Conference, pp.445-452, 2006.

O. Teytaud and S. Gelly, General lower bounds for evolutionary algorithms, Parallel Problem Solving from Nature (PPSN IX), pp.21-31, 2006.

H. Fournier and O. , Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign Patterns, Algorithmica, vol.XVI, issue.2, pp.387-408, 2011.
DOI : 10.1007/s00453-010-9391-3
URL : https://hal.archives-ouvertes.fr/inria-00452791

O. Teytaud, Lower Bounds for Evolution Strategies, pp.327-354, 2011.
DOI : 10.1142/9789814282673_0011
URL : https://hal.archives-ouvertes.fr/inria-00593179

J. Jägersküpper, Lower bounds for hit-andrun direct search, International Symposium on Stochastic Algorithms: Foundations and Applications (SAGA 2007), pp.118-129, 2007.

J. Jägersküpper, Lower bounds for randomized direct search with isotropic sampling, Operations Research Letters, vol.36, issue.3, pp.327-332, 2008.
DOI : 10.1016/j.orl.2007.10.003

J. Jägersküpper, Probabilistic runtime analysis of (1 + ,?) evolution strategies using isotropic mutations , Genetic and Evolutionary Computation Conference, pp.461-468, 2006.

M. Jebalia, A. Auger, and P. , Liardet: Log-linear convergence and optimal bounds for the (1+1)-ES, Evolution Artificielle (EA '07), pp.207-218, 2008.

A. Auger and N. Hansen, Theory of evolution strategies: A new perspective In: Theory of Randomized Search Heuristics: Foundations and Recent Developments, pp.289-325, 2011.

A. Auger, D. Brockhoff, and N. Hansen, Analyzing the impact of mirrored sampling and sequential selection in elitist evolution strategies, Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, FOGA '11, pp.127-138, 2011.
DOI : 10.1145/1967654.1967666
URL : https://hal.archives-ouvertes.fr/inria-00587507

A. Auger, D. Brockhoff, and N. Hansen, Mirrored sampling in evolution strategies with weighted recombination, Genetic and Evolutionary Computation Conference, pp.861-868, 2011.

D. V. Arnold and H. Beyer, Local performance of the (µ/µ I , ?)-ES in a noisy environment, Foundations of Genetic Algorithms (FOGA 6), pp.127-141, 2001.

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

D. V. Arnold, Noisy Optimization with Evolution Strategies, 2002.
DOI : 10.1007/978-1-4615-1105-2

D. V. Arnold and H. Beyer, Performance Analysis of Evolutionary Optimization With Cumulative Step Length Adaptation, IEEE Transactions on Automatic Control, vol.49, issue.4, pp.617-622, 2004.
DOI : 10.1109/TAC.2004.825637

A. I. Oyman and H. Beyer, Analysis of the (??/??, ??)-ES on the Parabolic Ridge, Evolutionary Computation, vol.8, issue.3, pp.267-289, 2000.
DOI : 10.1162/106365600750078772

D. V. Arnold and H. Beyer, Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge, Natural Computing, vol.2, issue.2, pp.555-587, 2008.
DOI : 10.1007/s11047-006-9025-5

D. V. Arnold and H. Beyer, On the Behaviour of Evolution Strategies Optimising Cigar Functions, Evolutionary Computation, vol.2004, issue.4, pp.661-682, 2010.
DOI : 10.1162/106365600750078781

H. Beyer, Toward a Theory of Evolution Strategies: Self-Adaptation, Evolutionary Computation, vol.2, issue.4, 1995.
DOI : 10.1162/evco.1993.1.2.165

S. Meyer-nieberg and H. Beyer, Mutative selfadaptation on the sharp and parabolic ridge, Foundations of Genetic Algorithms (FOGA 9), pp.70-96, 2007.

D. V. Arnold and A. Macleod, Hierarchically organised evolution strategies on the parabolic ridge, Genetic and Evolutionary Computation Conference, pp.437-444, 2006.

H. Beyer, M. Dobler, C. Hämmerle, and P. , Masser: On strategy parameter control by meta-ES, Genetic and Evolutionary Computation Conference, pp.499-506, 2009.

D. V. Arnold and A. Macleod, Step Length Adaptation on Ridge Functions, Evolutionary Computation, vol.16, issue.2, pp.151-184, 2008.
DOI : 10.1109/4235.728207

D. V. Arnold, On the use of evolution strategies for optimising certain positive definite quadratic forms, Genetic and Evolutionary Computation Conference, pp.634-641, 2007.

H. Beyer and S. Finck, Performance of the <formula formulatype="inline"><tex Notation="TeX">$(\mu /\mu _{I},\lambda )\hbox{-}\sigma {\rm SA}$</tex></formula>-ES on a Class of PDQFs, IEEE Transactions on Evolutionary Computation, vol.14, issue.3, pp.400-418, 2010.
DOI : 10.1109/TEVC.2009.2033581

M. Jebalia, A. Auger, and N. Hansen, Log-Linear Convergence and Divergence of??the??Scale-Invariant (1+1)-ES in Noisy Environments, Algorithmica, vol.13, issue.1, pp.425-460, 2011.
DOI : 10.1007/s00453-010-9403-3
URL : https://hal.archives-ouvertes.fr/inria-00433347

D. V. Arnold, H. V. Arnold, and H. Beyer, On the benefits of populations for noisy optimization A general noise model and its effects on evolution strategy performance, Evolutionary Computation IEEE Transactions on Evolutionary Computation, vol.11, issue.104, pp.111-127, 2003.

D. V. Arnold and H. Beyer, Optimum Tracking with Evolution Strategies, Evolutionary Computation, vol.14, issue.3, pp.291-308, 2006.
DOI : 10.1023/A:1010928206141

D. V. Arnold and D. Brauer, On the behaviour of the (1 + 1)-ES for a simple constrained problem, Parallel Problem Solving from, Nature, pp.1-10, 2008.

D. V. Arnold, On the behaviour of the (1,??)-es for a simple constrained problem, Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, FOGA '11, pp.15-24, 2011.
DOI : 10.1145/1967654.1967657

D. V. Arnold, Analysis of a repair mechanism for the (1, ?)-ES applied to a simple constrained problem, Genetic and Evolutionary Computation Conference, pp.853-860, 2011.

A. A. Zhigljavsky, Theory of Global Random Search, 1991.
DOI : 10.1007/978-94-011-3436-1

A. Bienvenüe and O. , Global convergence for evolution strategies in spherical problems: some simple proofs and difficulties, Theoretical Computer Science, vol.306, issue.1-3, pp.269-289, 2003.
DOI : 10.1016/S0304-3975(03)00284-6

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

J. Jägersküpper, Algorithmic analysis of a basic evolutionary algorithm for continuous optimization, Theoretical Computer Science, vol.379, issue.3, pp.329-347, 2007.
DOI : 10.1016/j.tcs.2007.02.042

J. Jägersküpper, How the (1+1) ES using isotropic mutations minimizes positive definite quadratic forms, Theoretical Computer Science, vol.361, issue.1, pp.38-56, 2006.
DOI : 10.1016/j.tcs.2006.04.004