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Original Article

Population subset selection for the use of a validation dataset for overfitting control in genetic programming

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Pages 243-271 | Received 23 Nov 2018, Accepted 11 Jul 2019, Published online: 31 Jul 2019

References

  • Archetti, F., Lanzeni, S., Messina, E., & Vanneschi, L. (2007). Genetic programming for computational pharmacokinetics in drug discovery and development. Genetic Programming and Evolvable Machines, 8(4), 413–432.
  • Azad, R., Medernach, D., & Ryan, C. (2014). Efficient interleaved sampling of training data in genetic programming. In Proceedings of the companion publication of the 2014 annual conference on genetic and evolutionary computation,  Vancouver, Canada (pp. 127–128).
  • Cavaretta, M. J., & Chellapilla, K. (1999). Data mining using genetic programming: The implications of parsimony on generalization error. In Evolutionary computation, 1999. cec 99. proceedings of the 1999 congress on, Washington, DC, USA, USA (Vol.2, pp. 1330–1337).
  • Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11(Jul), 2079–2107.
  • Danandeh Mehr, A., Kahya, E., Uyumaz, A., & Erdem, H. (2014, 5). Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach. Journal of Hydroinformatics, 16(6), 1318-1330. Doi:10.2166/hydro.2014.112.
  • Danandeh Mehr, A., & Nourani, V. (2017, 3). A pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling. Environmental Modelling and Software, 92, 239–251.
  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.
  • Dheeru, D., & Karra Taniskidou, E. (2017). UCI machine learning repository. Retrieved from http://archive.ics.uci.edu/ml
  • Ekárt, A., & Nemeth, S. Z. (2001). Selection based on the pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, 2(1), 61–73.
  • Foreman, N., & Evett, M. (2005). Preventing overfitting in gp with canary functions. In Proceedings of the 7th annual conference on genetic and evolutionary computation, Washington, DC, USA (pp. 1779–1780).
  • Gagné, C., Schoenauer, M., Parizeau, M., & Tomassini, M. (2006). Genetic programming, validation sets, and parsimony pressure. In European conference on genetic programming,  Budapest, Hungary (pp. 109–120).
  • Gathercole, C., & Ross, P. (1994). Dynamic training subset selection for supervised learning in genetic programming. In International conference on parallel problem solving from nature,Jerusalem, Israel. (pp. 312–321).
  • Gonçalves, I., & Silva, S. (2011). Experiments on controlling overfitting in genetic programming. In 15th portuguese conference on artificial intelligence (epia 2011), Lisbon, Portugal,  (pp. 10–13).
  • Gonçalves, I., & Silva, S. (2013). Balancing learning and overfitting in genetic programming with interleaved sampling of training data. In European conference on genetic programming, Vienna, Austria (pp. 73–84).
  • Gonçalves, I., Silva, S., Melo, J. B., & Carreiras, J. M. (2012). Random sampling technique for overfitting control in genetic programming. In European conference on genetic programming,  Málaga, Spain (pp. 218–229).
  • Gustafson, S., Ekárt, A., Burke, E., & Kendall, G. (2004). Problem difficulty and code growth in genetic programming. Genetic Programming and Evolvable Machines, 5(3), 271–290.
  • Igel, C. (2013, 06). A note on generalization loss when evolving adaptive pattern recognition systems. IEEE Transactions on Evolutionary Computation, 17, 345–352.
  • Langdon, W. (2011). Minimising testing in genetic programming. RN, 11(10), 1.
  • Li, L.-M., Lu, K.-D., Zeng, G.-Q., Wu, L., & Chen, M.-R. (2016). A novel real-coded population-based extremal optimization algorithm with polynomial mutation: A non- parametric statistical study on continuous optimization problems. Neurocomputing, 174, 577–587.
  • Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: An optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7–8), 1867–1877.
  • Liu, Y., & Khoshgoftaar, T. (2004). Reducing overfitting in genetic programming models for software quality classification. In null, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings.56–65. DOI:10.1109/HASE.2004.1281730.
  • Poli, R., & McPhee, N. F. (2014). Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP. In: Borenstein Y., Moraglio A. (eds) Theory and Principled Methods for the Design of Metaheuristics. Natural Computing Series. Springer, Berlin, Heidelberg. Doi:10.1007/978-3-642-33206-7_9 .
  • Robilliard, D., & Fonlupt, C. (2001). Backwarding: An overfitting control for genetic programming in a remote sensing application. In International conference on artificial evolution (evolution artificielle), Le Creusot, France, (pp. 245–254).
  • Silva, S., & Costa, E. (2009). Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines, 10(2), 141–179.
  • Soule, T., & Foster, J. A. (1998). Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4), 293–309.
  • Uy, N. Q., Hien, N. T., Hoai, N. X., & ONeill, M. (2010). Improving the generalisation ability of genetic programming with semantic similarity based crossover. In European conference on genetic programming,  Istanbul, Turkey (pp. 184–195).
  • Vanneschi, L., Castelli, M., & Silva, S. (2010). Measuring bloat, overfitting and functional complexity in genetic programming. In Proceedings of the 12th annual conference on genetic and evolutionary computation, Portland, OR, USA (pp. 877–884).
  • Vanneschi, L., & Gustafson, S. (2009). Using crossover based similarity measure to improve genetic programming generalization ability. In Proceedings of the 11th annual conference on genetic and evolutionary computation, Montreal, QC, Canada (pp. 1139–1146).
  • Vanneschi, L., & Silva, S. (2009). Using operator equalisation for prediction of drug toxicity with genetic programming. In Portuguese conference on artificial intelligence, Aveiro, Portuga (pp. 65–76).
  • Žegklitz, J., & Pošík, P. (2015). Model selection and overfitting in genetic programming: Empirical study. In Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation, Madrid, Spain (pp. 1527–1528).
  • Zeng, G.-Q., Xie, X.-Q., Chen, M.-R., & Weng, J. (2019). Adaptive population extremal optimization-based pid neural network for multivariable nonlinear control systems. Swarm and Evolutionary Computation, 44, 320–334.
  • Zhang, B.-T., & Mühlenbein, H. (1995). Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1), 17–38.

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