Y. Akimoto, A. Auger, and N. Hansen, Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic $\mathcal C^2$ -composite Functions, Parallel Problem Solving from Nature - PPSN XII, 12th International Conference, number 7491 in Lecture Notes in Computer Science, pp.42-51, 2012.
DOI : 10.1007/978-3-642-32937-1_5

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, 11th International Conference, 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

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

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

P. D. Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, A tutorial on the cross-entropy method, Annals of Operations Research, issue.134, pp.19-67, 2005.

V. S. Borkar, Stochastic approximation, Resonance, vol.8, issue.s.471012, 2008.
DOI : 10.1007/s12045-013-0136-x

P. Dayan and G. E. Hinton, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol.8, issue.2, pp.271-278, 1997.
DOI : 10.1016/0004-3702(89)90049-0

M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.26, issue.1, pp.1-13, 1996.
DOI : 10.1109/3477.484436

N. Hansen, S. D. Muller, 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

G. E. Hinton, Connectionist learning procedures, Artificial Intelligence, vol.40, issue.1-3, pp.185-234, 1989.
DOI : 10.1016/0004-3702(89)90049-0

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. Kullback, Information theory and statistics, 1968.

H. J. Kushner and G. G. Yin, Stochastic approximation and recursive algorithms and applications, 2003.

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

L. Malagò, M. Matteucci, and G. Pistone, Towards the geometry of estimation of distribution algorithms based on the exponential family, Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, FOGA '11, pp.230-242, 2011.
DOI : 10.1145/1967654.1967675

F. Sehnke, C. Osendorfer, T. Rückstieß, A. Graves, J. Peters et al., Parameter-exploring policy gradients, Neural Networks, vol.23, issue.4, pp.551-559, 2010.
DOI : 10.1016/j.neunet.2009.12.004

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=