S. Astete-morales, M. Cauwet, and O. Teytaud, Evolution Strategies with Additive Noise, Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, p.9, 2015.
DOI : 10.1145/2725494.2725500
URL : https://hal.archives-ouvertes.fr/hal-01077625

H. Beyer, Mutate large, but inherit small! On the analysis of rescaled mutations in ( $$\tilde 1,\tilde \lambda$$ )-ES with noisy fitness data
DOI : 10.1007/BFb0056854

H. F. Chen, T. E. Duncan, and B. Pasik-duncan, A stochastic approximation algorithm with random differences, Proceedings of the 13th IFAC World Congress, pp.493-496, 1996.

J. Decock and O. Teytaud, Noisy optimization complexity under locality assumption, Proceedings of the twelfth workshop on Foundations of genetic algorithms XII, FOGA XII '13, pp.183-190, 2013.
DOI : 10.1145/2460239.2460256
URL : https://hal.archives-ouvertes.fr/hal-00755663

V. Dupa?, Notes on stochastic approximation methods, Czechoslovak Mathematical Journal, vol.08, issue.1, pp.139-149, 1958.

V. Fabian, Stochastic Approximation of Minima with Improved Asymptotic Speed, The Annals of Mathematical Statistics, vol.38, issue.1, pp.191-200, 1967.
DOI : 10.1214/aoms/1177699070

K. G. Jamieson, R. Nowak, and B. Recht, Query complexity of derivative-free optimization, Advances in Neural Information Processing Systems 25, pp.2672-2680, 2012.

J. Kiefer and J. Wolfowitz, Stochastic Estimation of the Maximum of a Regression Function, The Annals of Mathematical Statistics, vol.23, issue.3, pp.462-466, 1952.
DOI : 10.1214/aoms/1177729392

P. Rolet and O. Teytaud, Adaptive Noisy Optimization, Applications of Evolutionary Computation, pp.592-601
DOI : 10.1007/978-3-642-12239-2_61
URL : https://hal.archives-ouvertes.fr/inria-00459017

O. Shamir, On the complexity of bandit and derivative-free stochastic convex optimization, COLT 2013 -The 26th Annual Conference on Learning Theory, pp.3-24, 2013.

M. Zedek, Continuity and location of zeroes of linear combinations of polynomials, Proc. Amer, pp.78-84, 1965.