L. Breiman, Arcing classifiers, Annals of Statistics, vol.26, issue.3, pp.801-845, 1998.

A. Chandra and X. Yao, Ensemble Learning Using Multi-Objective Evolutionary Algorithms, Journal of Mathematical Modelling and Algorithms, vol.29, issue.2, pp.417-425, 2006.
DOI : 10.1007/s10852-005-9020-3

A. Chandra and X. Yao, Evolving hybrid ensembles of learning machines for better generalisation, Neurocomputing, vol.69, issue.7-9, pp.686-700, 2006.
DOI : 10.1016/j.neucom.2005.12.014

T. G. Dietterich, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, Neural Computation, vol.6, issue.7, pp.1895-1923, 1998.
DOI : 10.1007/BF00058655

T. G. Dietterich, Ensemble Methods in Machine Learning, First Int. Workshop on Multiple Classifier Systems, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

R. Esposito and L. Saitta, Monte Carlo theory as an explanation of Bagging and Boosting, Proc. of the Int. Joint Conf. on Artificial Intelligence (IJCAI'03), pp.499-504, 2003.

G. Folino, C. Pizzuti, and G. Spezzano, Ensemble Techniques for Parallel Genetic Programming Based Classifiers, Proc. of the European Conf. on Genetic Programming (EuroGP'03), pp.59-69, 2003.
DOI : 10.1007/3-540-36599-0_6

Y. Freund and R. Schapire, Experiments with a new Boosting algorithm, Proc. of the Int. Conf. on Machine Learning (ICML'96), pp.148-156, 1996.

C. Gagné and M. Parizeau, GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS: PRINCIPLES AND CASE-STUDY, International Journal on Artificial Intelligence Tools, vol.15, issue.02
DOI : 10.1142/S021821300600262X

C. Gagné and M. Parizeau, Open BEAGLE, ACM SIGEVOlution, vol.1, issue.1, 2006.
DOI : 10.1145/1138470.1138473

R. Gilad-bachrach, A. Navot, and N. Tishby, Margin based feature selection - theory and algorithms, Twenty-first international conference on Machine learning , ICML '04, pp.43-50, 2004.
DOI : 10.1145/1015330.1015352

W. D. Hillis, Co-evolving parasites improve simulated evolution as an optimization procedure, Physica D: Nonlinear Phenomena, vol.42, issue.1-3, pp.228-234, 1990.
DOI : 10.1016/0167-2789(90)90076-2

J. Holland, Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems, Machine Learning, pp.593-623, 1986.

J. Holmes, P. Lanzi, W. Stolzmann, and S. Wilson, Learning classifier systems: New models, successful applications, Information Processing Letters, vol.82, issue.1, pp.23-30, 2002.
DOI : 10.1016/S0020-0190(01)00283-6

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

H. Iba, Bagging, Boosting, and bloating in genetic programming, Proc. of the Genetic and Evolutionary Computation Conference (GECCO'99), pp.1053-1060, 1999.

M. Keijzer and V. Babovic, Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff ??? Introductory Investigations, Proc. of the European Conf. on Genetic Programming (EuroGP'00), pp.76-90, 2000.
DOI : 10.1007/978-3-540-46239-2_6

Y. Liu, X. Yao, and T. Higuchi, Evolutionary ensembles with negative correlation learning, IEEE Trans. on Evolutionary Computation, vol.4, issue.4, pp.380-387, 2000.

D. Newman, S. Hettich, C. Blake, and C. Merz, UCI repository of machine learning databases, 1998.

J. Paredis, Coevolving cellular automata: Be aware of the Red Queen!, Proc. of the Int. Conf. on Genetic Algorithms (ICGA'97), pp.393-400, 1997.

G. Paris, D. Robilliard, and C. Fonlupt, Applying Boosting Techniques to Genetic Programming, In Artificial Evolution LNCS, vol.2310, pp.267-278, 2001.
DOI : 10.1007/3-540-46033-0_22

C. Rudin, I. Daubechies, and R. E. Schapire, The dynamics of AdaBoost: Cyclic behavior and convergence of margins, J. of Machine Learning Research, vol.5, pp.1557-1595, 2004.

R. Schapire, Y. Freund, P. Bartlett, and W. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, pp.1651-1686, 1998.
DOI : 10.1214/aos/1024691352

D. Song, M. I. Heywood, and A. N. Zincir-heywood, Training Genetic Programming on Half a Million Patterns: An Example From Anomaly Detection, IEEE Transactions on Evolutionary Computation, vol.9, issue.3, pp.225-239, 2005.
DOI : 10.1109/TEVC.2004.841683

V. N. Vapnik, Statistical Learning Theory, 1998.