E. Galván-lópez and O. A. Elhara, Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016.
DOI : 10.1109/SSCI.2016.7849970

E. Galván-lópez, E. Mezura-montes, O. A. Elhara, and M. Schoenauer, On the Use of Semantics in Multi-objective Genetic Programming, Parallel Problem Solving from Nature ? PPSN XIV: 14th International Conference Proceedings, pp.353-363, 2016.
DOI : 10.1007/978-3-540-70928-2_64

E. Galván-lópez, L. Vázquez-mendoza, M. Schoenauer, and L. Trujillo, Dynamic GP fitness cases in static and dynamic optimisation problems, Proceedings of the Genetic and Evolutionary Computation Conference Companion on , GECCO '17, pp.227-228, 2017.
DOI : 10.1109/SSCI.2016.7849970

E. Galván-lópez, L. Vázquez-mendoza, and L. Trujillo, Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data, Advances in Soft Computing, chapter 22, pp.261-272, 2016.
DOI : 10.1007/s10710-013-9210-0

C. Gathercole and P. Ross, Dynamic training subset selection for supervised learning in Genetic Programming, Parallel Problem Solving from Nature III, pp.312-321, 1994.
DOI : 10.1007/3-540-58484-6_275

URL : ftp://ftp.dai.ed.ac.uk/pub/user/ga/94-006.ps.Z

M. Giacobini, M. Tomassini, and L. Vanneschi, Limiting the Number of Fitness Cases in Genetic Programming Using Statistics, Parallel Problem Solving from Nature VII, pp.371-380, 2002.
DOI : 10.1007/3-540-45712-7_36

X. , .. I. Gonçalves, and S. Silva, Balancing learning and overfitting in genetic programming with interleaved sampling of training data, Genetic Programming, pp.73-84, 2013.

T. Jones and S. Forrest, Fitness distance correlation as a measure of problem difficulty for genetic algorithms, Proceedings of the Sixth International Conference on Genetic Algorithms, pp.184-192, 1995.

J. R. Koza, Genetic programming as a means for programming computers by natural selection, Statistics and Computing, vol.4, issue.2, 1992.
DOI : 10.1007/BF00175355

W. , L. Cava, L. Spector, and K. Danai, Epsilon-lexicase selection for regression, Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp.741-748, 2016.

C. W. Lasarczyk, P. W. Dittrich, and W. W. Banzhaf, Dynamic Subset Selection Based on a Fitness Case Topology, Evolutionary Computation, vol.10, issue.8, pp.223-242, 2004.
DOI : 10.2307/2410639

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

U. López, L. Trujillo, Y. Martinez, P. Legrand, E. Naredo et al., Ransacgp: Dealing with outliers in symbolic regression with genetic programming, Genetic Programming: 20th European Conference Proceedings, pp.114-130, 2017.

J. Macedo, E. Costa, and L. Marques, Genetic Programming Algorithms for Dynamic Environments, pp.280-295
DOI : 10.1007/978-3-319-31153-1_19

Y. Martnez, E. Naredo, L. Trujillo, P. Legrand, and U. Lpez, A comparison of fitness-case sampling methods for genetic programming, Journal of Experimental & Theoretical Artificial Intelligence, vol.22, issue.6, pp.1-22, 2017.
DOI : 10.1016/j.patrec.2005.07.024

J. Mcdermott, Genetic programming needs better benchmarks, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.791-798
DOI : 10.1145/2330163.2330273

T. T. Nguyen, S. Yang, and J. Branke, Evolutionary dynamic optimization: A survey of the state of the art, Swarm and Evolutionary Computation, vol.6, pp.1-24, 2012.
DOI : 10.1016/j.swevo.2012.05.001

M. Riekert, K. M. Malan, and A. P. Engelbrect, Adaptive Genetic Programming for dynamic classification problems, 2009 IEEE Congress on Evolutionary Computation, pp.674-681, 2009.
DOI : 10.1109/CEC.2009.4983010

L. Spector, Assessment of problem modality by differential performance of lexicase selection in genetic programming, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion '12, pp.401-408, 2012.
DOI : 10.1145/2330784.2330846

A. Teller and D. Andre, Automatically choosing the number of fitness cases: The rational allocation of trials, Genetic Programming 1997: Proceedings of the Second Annual Conference, pp.321-328, 1997.

L. Vanneschi and G. Cuccu, A study of genetic programming variable population size for dynamic optimization problems, IJCCI, pp.119-126, 2009.

N. Wagner, Z. Michalewicz, M. Khouja, and R. R. Mcgregor, Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model, IEEE Transactions on Evolutionary Computation, vol.11, issue.4, pp.433-452, 2007.
DOI : 10.1109/TEVC.2006.882430

URL : http://www.cs.adelaide.edu.au/~zbyszek/Papers/DyForGP.pdf

B. Zhang and D. Cho, Genetic Programming with Active Data Selection, Simulated Evolution and Learning on Simulated Evolution and Learning, SEAL'98, pp.146-153, 1999.
DOI : 10.1007/3-540-48873-1_20

URL : http://bi.snu.ac.kr/Publications/Journals/International/LNAI1585.pdf