[1] Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT press, Cambridge (1992)

[2] Agapitos, A., Brabazon, A., O’Neill, M.: Controlling overfitting in symbolic regression based on a bias/variance error decomposition. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 438–447. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32937-1_44

[3] Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 183–187 (1985)

[4] Nordin, P.: Genetic programming iii-darwinian invention and problem solving. Evol. Comput. 7, 451–453 (1999)

[5] Cohen, P.R.: Empirical Methods for Artificial Intelligence, vol. 139. MIT press, Cambridge (1995)

[6] Hansen, J.V., Lowry, P.B., Meservy, R.D., McDonald, D.M.: Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection. Decis. Support Syst. 43, 1362–1374 (2007)

[7] Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

[8] Fitzgerald, J., Azad, R., Ryan, C.: A bootstrapping approach to reduce over-fitting in genetic programming. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1113–1120. ACM (2013)

[9] Gonçalves, I., Silva, S., Melo, J.B., Carreiras, J.M.B.: Random sampling technique for overfitting control in genetic programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 218–229. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29139-5_19

[10] Gonçalves, I., Silva, S.: Balancing learning and overfitting in genetic programming with interleaved sampling of training data. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 73–84. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37207-0_7

[11] Nguyen, T.H., Nguyen, X.H., McKay, B., Nguyen, Q.U.: Where should we stop? An investigation on early stopping for GP learning. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL 2012. LNCS, vol. 7673, pp. 391–399. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34859-4_39

[12] Uy, N.Q., Hien, N.T., Hoai, N.X., O’Neill, M.: Improving the generalisation ability of genetic programming with semantic similarity based crossover. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184–195. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12148-7_16

[13] Muttil, N., Chau, K.-W.: Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut. 28, 223–238 (2006). Inderscience Publishers

[14] Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for human oral bioavailability of drugs. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 255–262. ACM (2006)

[15] Juang, C.-F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34, 997–1006 (2004)

[16] Whigham, P.A., Crapper, P.F.: Time series modelling using genetic programming: an application to rainfall-runoff models. In: Advances in Genetic Programming, vol. 3, pp. 89–104. MIT Press, Cambridge (1999)

[17] Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2005). The Mathematical Intelligencer, 27, 83–85. Springer