J. Arabas, O. Maitre, and P. Collet, PARADE: A Massively Parallel Differential Evolution Template for EASEA, Swarm and Evolutionary Computation, pp.12-20
DOI : 10.1007/978-3-642-29353-5_2

D. Ardia, J. O. Arango, and N. G. Gomez, Jump-Diffusion Calibration Using Differential Evolution, Wilmott, vol.2011, issue.55, pp.76-79, 2011.
DOI : 10.1002/wilm.10034

D. Ardia, K. Boudt, P. Carl, K. M. Mullen, and B. G. Peterson, Differential Evolution with DEoptim: An application to non-convex portfolio optimization, The R Journal, vol.3, issue.1, pp.27-34, 2011.

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

A. Auger, M. Jebalia, and O. Teytaud, Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies, p.12, 2005.
DOI : 10.1007/11740698_26

URL : https://hal.archives-ouvertes.fr/inria-00000544

H. Beyer, The Theory of Evolution Strategies. Natural Computing Series, 2001.

D. Bratton and J. Kennedy, Defining a Standard for Particle Swarm Optimization, 2007 IEEE Swarm Intelligence Symposium, pp.120-127, 2007.
DOI : 10.1109/SIS.2007.368035

J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems, IEEE Transactions on Evolutionary Computation, vol.10, issue.6, pp.646-657, 2006.
DOI : 10.1109/TEVC.2006.872133

J. Chang, S. Chu, J. F. Roddick, and J. Pan, A parallel particle swarm optimization algorithm with communication strategies, J. Inf. Sci. Eng, vol.21, issue.4, pp.809-818, 2005.

M. Clerc, Beyond standard particle swarm optimisation, IJSIR, vol.1, issue.4, pp.46-61, 2010.

M. Clerc and J. Kennedy, The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, vol.6, issue.1, pp.58-73, 2002.
DOI : 10.1109/4235.985692

L. Devroye, L. Györfi, and G. Lugosi, A probabilistic Theory of Pattern Recognition, 1997.
DOI : 10.1007/978-1-4612-0711-5

H. Fournier and O. Teytaud, Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign Patterns, Algorithmica, vol.XVI, issue.2, 2010.
DOI : 10.1007/s00453-010-9391-3

URL : https://hal.archives-ouvertes.fr/inria-00452791

M. Gardner, A. W. Mcnabb, and K. D. Seppi, A speculative approach to parallelization in particle swarm optimization, Swarm Intelligence, vol.1, issue.1, pp.77-116, 2012.
DOI : 10.1007/s11721-011-0066-8

S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.2, pp.482-500, 2012.
DOI : 10.1109/TSMCB.2011.2167966

J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

J. Liu and J. Lampinen, A Fuzzy Adaptive Differential Evolution Algorithm, Soft Computing, vol.9, issue.6, pp.448-462, 2005.
DOI : 10.1007/s00500-004-0363-x

B. Mahdad, K. Srairi, T. Bouktir, and M. Benbouzid, Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch, IEEE Proceedings of the 2010 IEEE International Conference of the IEEE Industrial Electronics Society, pp.2695-2701, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00564733

R. Mallipeddi, P. Suganthan, Q. Pan, and M. Tasgetiren, Differential evolution algorithm with ensemble of parameters and mutation strategies, <ce:title>The Impact of Soft Computing for the Progress of Artificial Intelligence</ce:title>, pp.1679-1696, 2011.
DOI : 10.1016/j.asoc.2010.04.024

A. Mcnabb, C. Monson, and K. Seppi, Parallel PSO using MapReduce, 2007 IEEE Congress on Evolutionary Computation, pp.7-14, 2007.
DOI : 10.1109/CEC.2007.4424448

J. Olensek, T. Tuma, and J. Puhan, A new asynchronous parallel global optimization method based on simulated annealing and differential evolution, Applied Soft Computing, vol.11, issue.1, pp.1481-1489, 2011.
DOI : 10.1016/j.asoc.2010.04.019

K. E. Parsopoulos and M. N. Vrahatis, Parameter selection and adaptation in Unified Particle Swarm Optimization, Mathematical and Computer Modelling, vol.46, issue.1-2, pp.198-213, 2007.
DOI : 10.1016/j.mcm.2006.12.019

M. E. Pedersen, Tuning & simplifying heuristical optimization, 2010.

P. Po?ík and V. Klem?, Jade, an adaptive differential evolution algorithm, benchmarked on the bbob noiseless testbed, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion '12, pp.197-204

K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution -A Practical Approach to Global Optimization. Natural Computing, 2006.

A. K. Qin, F. Raimondo, F. Forbes, and Y. S. Ong, An improved CUDA-based implementation of differential evolution on GPU, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.991-998, 2012.
DOI : 10.1145/2330163.2330301

URL : https://hal.archives-ouvertes.fr/hal-00780081

I. Rechenberg, Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution, 1973.

E. Samansky, Zaslavsky's theorem. Univ. Rice website, 2002.

J. F. Schutte, J. A. Reinbolt, B. J. Fregly, R. T. Haftka, and A. D. George, Parallel global optimization with the particle swarm algorithm, International Journal for Numerical Methods in Engineering, vol.28, issue.13, pp.2296-2315, 2003.
DOI : 10.1002/nme.1149

Y. Shi and R. C. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69-73, 1998.
DOI : 10.1109/ICEC.1998.699146

R. Storn and K. Price, Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol.11, issue.4, pp.341-359, 1997.
DOI : 10.1023/A:1008202821328

F. Teytaud and O. Teytaud, Log(lambda) Modifications for Optimal Parallelism, Parallel Problem Solving From Nature, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00495087

O. Teytaud and S. Gelly, General Lower Bounds for Evolutionary Algorithms, 10 th International Conference on Parallel Problem Solving from Nature, 2006.
DOI : 10.1007/11844297_3

URL : https://hal.archives-ouvertes.fr/inria-00112820

I. C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, vol.85, issue.6, pp.317-325, 2003.
DOI : 10.1016/S0020-0190(02)00447-7

URL : https://hal.archives-ouvertes.fr/hal-01313364

V. N. Vapnik, The Nature of Statistical Learning, 1995.

M. Weber, F. Neri, and V. Tirronen, Parallel Random Injection Differential Evolution, Applications of Evolutionary Computation, pp.471-480, 2010.
DOI : 10.1007/978-3-642-12239-2_49

M. Yang, J. Guan, Z. Cai, and L. Wang, Self-adapting Differential Evolution Algorithm with Chaos Random for Global Numerical Optimization, Advances in Computation and Intelligence, pp.112-122, 2010.
DOI : 10.1007/978-3-642-16493-4_12

W. Yu and J. Zhang, Multi-population differential evolution with adaptive parameter control for global optimization, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.1093-1098, 2011.
DOI : 10.1145/2001576.2001724

M. Zambrano-bigiarini, M. Clerc, and R. Rojas, Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements, 2013 IEEE Congress on Evolutionary Computation, pp.2337-2344, 2013.
DOI : 10.1109/CEC.2013.6557848

J. Zhang and A. C. Sanderson, JADE: Adaptive Differential Evolution With Optional External Archive, IEEE Transactions on Evolutionary Computation, vol.13, issue.5, pp.945-958, 2009.
DOI : 10.1109/TEVC.2009.2014613