D. Aldous and U. Vazirani, "Go with the winners" algorithms, Proceedings 35th Annual Symposium on Foundations of Computer Science, pp.492-501, 1994.
DOI : 10.1109/SFCS.1994.365742

R. Assaraf, M. Caffarel, and A. Khelif, Diffusion Monte Carlo methods with a fixed number of walkers, Physical Review E, vol.61, issue.4, pp.4566-4575
DOI : 10.1103/PhysRevE.61.4566

J. Carpenter, P. Clifford, and P. Fearnhead, An improved particle filter for non-linear problems, IEE Proceedings F, vol.146, pp.2-7, 1999.

R. Cerf, Asymptotic convergence of genetic algorithms, Advances in Applied Probability, vol.32, issue.02, pp.521-550, 1998.
DOI : 10.1007/BF01295225

F. Cérou, P. D. Moral, and A. Guyader, A non asymptotic variance theorem for unnormalized Feynman-Kac particle models, 2008.

F. Cérou, P. Del-moral, F. Le-gland, and P. Lézaud, Genealogical models in entrance times rare event analysis, Alea, 2006.

N. Chopin, A sequential particle filter method for static models, Biometrika, vol.89, issue.3, pp.539-552, 2002.
DOI : 10.1093/biomet/89.3.539

P. and D. Moral, Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications, 2004.

P. , D. Moral, A. Doucet, and A. Jasra, Sequential Monte Carlo samplers, J. Royal Statist. Soc. B, vol.68, pp.411-436, 2006.

P. , D. Moral, and E. Rio, Concentration inequalities for mean field particle models, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00375134

P. , D. Moral, and L. Miclo, Annealed Feynman-Kac Models, Communications in Mathematical Physics, vol.235, issue.2, pp.191-214, 2003.
DOI : 10.1007/s00220-003-0802-z

URL : https://hal.archives-ouvertes.fr/hal-01469193

P. , D. Moral, and A. Guionnet, On the stability of measure valued processes with applications to filtering, C. R. Acad. Sci. Paris Sér. I Math, vol.329, pp.429-434, 1999.

P. , D. Moral, and A. Doucet, Particle motions in absorbing medium with hard and soft obstacles, Stochastic Anal. Appl, vol.22, pp.1175-1207, 2004.

P. , D. Moral, and L. Miclo, Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman-Kac semigroups. ESAIM: Probability and Statistics, pp.171-208, 2003.

P. , D. Moral, and L. Miclo, Genealogies and Increasing Propagations of Chaos for Feynman-Kac and Genetic Models, Annals of Applied Probability, vol.11, issue.4, pp.1166-1198, 2001.

A. Doucet and A. M. Johansen, A tutorial on particle filtering and smoothing: fifteen years later. in Handbook of Nonlinear Filtering, 2009.

A. Doucet, S. J. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 2000.
DOI : 10.1023/A:1008935410038

P. Fearnhead, Computational methods for complex stochastic systems: a review of some alternatives to MCMC, Statistics and Computing, vol.12, issue.2, pp.151-171, 2008.
DOI : 10.1007/s11222-007-9045-8

H. R. Künsch, State Space and Hidden Markov Models, pp.109-173, 2001.
DOI : 10.1201/9781420035988.ch3

P. Glasserman, P. Heidelberger, P. Shahabuddin, and T. Zajic, Multilevel Splitting for Estimating Rare Event Probabilities, Operations Research, vol.47, issue.4, pp.585-600, 1999.
DOI : 10.1287/opre.47.4.585

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3637

D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

N. J. Gordon, D. Salmond, and A. F. Smith, A novel approach to state estimation to nonlinear non-Gaussian state estimation, IEE Proceedings F, vol.40, pp.107-113, 1993.

P. Grassberger, Pruned-enriched Rosenbluth method: Simulations of ? polymers of chain length up to 1 000 000, Phys. Rev. E, pp.3682-3693, 1997.

J. H. Hetherington, Observations on the statistical iteration of matrices, Physical Review A, vol.30, issue.5, pp.2713-2719, 1984.
DOI : 10.1103/PhysRevA.30.2713

J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.

A. M. Johansen, P. , and A. Doucet, Sequential Monte Carlo Samplers for Rare Events, Proceedings of 6th International Workshop on Rare Event Simulation, 2006.

G. Kitagawa, Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, J. Comp. Graph. Statist, vol.5, pp.1-25, 1996.

J. S. Liu, Monte Carlo Strategies in Scientific Computing, 2001.
DOI : 10.1007/978-0-387-76371-2

J. S. Liu and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, vol.24, issue.443, pp.1032-1044, 1998.
DOI : 10.1073/pnas.94.26.14220

V. Melik-alaverdian and M. P. Nightingale, QUANTUM MONTE CARLO METHODS IN STATISTICAL MECHANICS, INRIA Centre de recherche INRIA Bordeaux ? Sud Ouest Domaine Universitaire -351, cours de la Libération -33405 Talence Cedex, pp.1409-1418, 1999.
DOI : 10.1142/S0129183199001182

I. Centre-de-recherche, ?. Grenoble, and . Rhône-alpes, Europe -38334 Montbonnot Saint-Ismier Centre de recherche INRIA Lille ? Nord Europe : Parc Scientifique de la Haute Borne -40, avenue Halley -59650 Villeneuve d'Ascq Centre de recherche INRIA Nancy ? Grand Est : LORIA, Technopôle de Nancy-Brabois -Campus scientifique 615, rue du Jardin Botanique -BP 101 -54602 Villers-lès-Nancy Cedex Centre de recherche INRIA Paris ? Rocquencourt : Domaine de Voluceau -Rocquencourt -BP 105 -78153 Le Chesnay Cedex Centre de recherche INRIA Rennes ? Bretagne Atlantique : IRISA, Campus universitaire de Beaulieu -35042 Rennes Cedex Centre de recherche INRIA Saclay ? Île-de-France, des Vignes : 4, rue Jacques Monod -91893 Orsay Cedex Centre de recherche INRIA, pp.105-78153, 2004.