Y. Akimoto, Y. Nagata, I. Ono, and S. Kobayashi, Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies, Parallel Problem Solving from Nature ? PPSN XI, pp.154-163, 2010.
DOI : 10.1007/978-3-642-15844-5_16

S. Amari, Natural Gradient Works Efficiently in Learning, Neural Computation, vol.37, issue.2, pp.251-276, 1998.
DOI : 10.1103/PhysRevLett.76.2188

S. Amari and H. Nagaoka, Methods of Information Geometry. Methods of Information Geometry, 2007.

S. Amari, H. Park, and K. Fukumizu, Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons, Neural Computation, vol.12, issue.6, pp.1399-1409, 2000.
DOI : 10.1162/089976698300017007

L. Arnold, A. Auger, N. Hansen, and Y. Ollivier, Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00601503

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

S. Baluja and R. Caruana, Removing the Genetics from the Standard Genetic Algorithm, Proceedings of the 12th International Conference on Machine Learning, pp.38-46, 1995.
DOI : 10.1016/B978-1-55860-377-6.50014-1

S. Bhatnagar, R. S. Sutton, M. Ghavamzadeh, and M. Lee, Natural actor???critic algorithms, Automatica, vol.45, issue.11, pp.2471-2482, 2009.
DOI : 10.1016/j.automatica.2009.07.008

URL : https://hal.archives-ouvertes.fr/hal-00840470

P. D. Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, A Tutorial on the Cross-Entropy Method, Annals of Operations Research, pp.19-67, 2005.

T. Glasmachers, T. Schaul, S. Yi, D. Wierstra, and J. Schmidhuber, Exponential natural evolution strategies, Proceedings of the 12th annual conference on Genetic and evolutionary computation, GECCO '10, pp.393-400, 2010.
DOI : 10.1145/1830483.1830557

URL : http://infoscience.epfl.ch/record/163869

E. Greensmith, P. L. Bartlett, and J. Baxter, Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning, Journal of Machine Learning Research, vol.5, pp.1471-1530, 2004.

N. Hansen and S. Kern, Evaluating the CMA Evolution Strategy on Multimodal Test Functions, Parallel Problem Solving from Nature -PPSN VIII, pp.282-291, 2004.
DOI : 10.1007/978-3-540-30217-9_29

N. Hansen, S. D. Müller, and P. Koumoutsakos, Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Evolutionary Computation, vol.11, issue.1, pp.1-18, 2003.
DOI : 10.1162/106365601750190398

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

D. A. Harville, Matrix Algebra from a Statistician's Perspective, 2008.

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

P. Larrañaga and J. A. Lozano, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, 2002.
DOI : 10.1007/978-1-4615-1539-5

L. Mirsky, A trace inequality of John von Neumann, Monatshefte für Mathematik, pp.303-306, 1975.
DOI : 10.1007/BF01647331

J. Nocedal and S. J. Wright, Numerical Optimization. Springer Series in Operations Research, 2006.

H. Park, S. Amari, and K. Fukumizu, Adaptive natural gradient learning algorithms for various stochastic models, Neural networks : the official journal of the International Neural Network Society, pp.755-764, 2000.
DOI : 10.1016/S0893-6080(00)00051-4

J. Peters and S. Schaal, Natural Actor-Critic, Neurocomputing, vol.71, issue.7-9, pp.1180-1190, 2008.
DOI : 10.1016/j.neucom.2007.11.026

M. Rattray, D. Saad, and S. Amari, Natural gradient descent for on-line learning. Physical review letters, pp.5461-5464, 1998.