J. C. Schlimmer and R. H. Granger, Incremental learning from noisy data, Machine Learning, pp.317-354, 1986.
DOI : 10.1007/BF00116895

R. Elwell and R. Polikar, Incremental Learning of Concept Drift in Nonstationary Environments, IEEE Transactions on Neural Networks, vol.22, issue.10, pp.1517-1531, 2011.
DOI : 10.1109/TNN.2011.2160459

L. I. Kuncheva, Classifier Ensembles for Changing Environments, Multiple Classifier Systems, 2004.
DOI : 10.1007/978-3-540-25966-4_1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.388

L. I. Kuncheva, Classifier ensemble for detecting concept change in streaming data: Overview and perspectives, Proc. Eur. Conf. Artif. Intell, pp.5-10, 2008.

M. T. Ahiskali, M. Muhlbaier, M. Polikar, and R. , Learning concept drift in non-stationary environments using an ensemble of classifiers based approach, IJCNN, pp.3455-3462, 2008.

A. V. Abs-da-cruz, M. M. Vellasco, and M. A. Pacheco, Quantum-Inspired Evolutionary Algorithms applied to numerical optimization problems, IEEE Congress on Evolutionary Computation, 2006.
DOI : 10.1109/CEC.2010.5586193

A. V. Abs-da-cruz, Algoritmos evolutivos com inspiração quântica para otimização de problemas com representação numérica, 2007.

K. Han and J. Kim, Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Transactions on Evolutionary Computation, vol.6, issue.6, pp.580-593, 2002.
DOI : 10.1109/TEVC.2002.804320

K. Han and J. Kim, On Setting the Parameters of QEA for Practical Applications: Some Guidelines Based on Empirical Evidence, GECCO, Lecture Notes in Computer Science, vol.2723, pp.427-428, 2003.
DOI : 10.1007/3-540-45105-6_52

K. Han and J. Kim, Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion,<tex>$H_epsilon $</tex>Gate, and Two-Phase Scheme, IEEE Transactions on Evolutionary Computation, vol.8, issue.2, pp.156-169, 2004.
DOI : 10.1109/TEVC.2004.823467

W. N. Street and Y. Kim, A streaming ensemble algorithm (SEA) for large-scale classification, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '01, pp.377-382, 2001.
DOI : 10.1145/502512.502568

R. Polikar and R. Elwell, Benchmark Datasets for Evaluating Concept drift/NSE Algorithms At: http://users.rowan, 2012.

D. C. Montgomery, Design and analysis of experiments, 2008.

R. Development and C. Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Donwload at: www.r-project.org, 2012.

P. Royston, An Extension of Shapiro and Wilk's W Test for Normality to Large Samples, Applied Statistics, vol.31, issue.2, pp.115-124, 1982.
DOI : 10.2307/2347973

J. Kolter and M. Maloof, Dynamic weighted majority: An ensemble method for drifting concepts, Journal of Machine Learning Research, vol.8, pp.2755-2790, 2007.

K. Jackowski, Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers, Pattern Analysis and Applications, vol.10, issue.7, 2013.
DOI : 10.1007/s10044-013-0318-x