A comparison of fitness-case sampling methods for genetic programming

References

• Derrac, J., Garca, 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, 3–18.
   Web of Science ®Google Scholar
• Doucette, J., & Heywood, M. I. (2008). Gp classification under imbalanced data sets: Active sub-sampling and auc approximation. In Proceedings of the 11th European conference on genetic programming (pp. 266–277). Berlin, Heidelberg: Springer- Verlag.
   Google Scholar
• Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.
   Web of Science ®Google Scholar
• Gathercole, C., & Ross, P. (1994a). Dynamic training subset selection for supervised learning in genetic programming. In Proceedings of the international conference on evolutionary computation. The third conference on parallel problem solving from nature: Parallel problem solving from nature (pp. 312–321). London: Springer-Verlag.
   Google Scholar
• Gathercole, C., & Ross, P. (1994b). Some training subset selection methods for supervised learning in genetic programming. Presented at ECAI94 workshop on applied genetic and other evolutionary algorithms.
   Google Scholar
• Giacobini, M., Tomassini, M., & Vanneschi, L. (2002). Limiting the number of fitness cases in genetic programming using statistics. In Proceedings of the 7th international conference on parallel problem solving from nature (pp. 371–380). London: Springer-Verlag.
   Google Scholar
• Gonçalves, I., & Silva, S. (2011, October 10--13). Experiments on controlling overfitting in genetic programming. In Proceedings in artificial intelligence, 15th portuguese conference on artificial intelligence, EPIA 2011. Lisbon, Portugal: Springer.
   Google Scholar
• Gonçalves, I., & Silva, S. (2013). Balancing learning and overfitting in genetic programming with interleaved sampling of training data. In K. Krawiec, A. Moraglio, T. Hu, A. Etaner-Uyar, & B. Hu (Eds.), Genetic programming (Vol. 7831, pp. 73–84). Berlin Heidelberg: Springer.
   Google Scholar
• Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.
   Web of Science ®Google Scholar
• Koza, J. (2010). Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11, 251–284.
   Web of Science ®Google Scholar
• Lasarczyk, C. W. G., Dittrich, P. W. G., & Banzhaf, W. W. G. (2004). Dynamic subset selection based on a fitness case topology. Evolutionary Computation, 12, 223–242.
   PubMed Web of Science ®Google Scholar
• Lichman, M. (2013). UCI machine learning repository. Retrieved from http://archive.ics.uci.edu/ml
   Google Scholar
• Martínez, Y., Naredo, E., Trujillo, L., & Galván-López, E. (2013, June). Searching for novel regression functions. In 2013 IEEE congress on evolutionary computation (pp. 16–23), Canc\’{u}n, M\’{e}xico.
   Google Scholar
• Martínez, Y., Trujillo, L., Naredo, E. & Legrand,, P. (2014). A comparison of fitness- case sampling methods for symbolic regression with genetic programming. In A. A. Tantar (Ed.), Evolve -- A bridge between probability, set oriented numerics, and evolutionary computation v (Vol. 288, pp. 201–212). Springer International Publishing.
   Google Scholar
• McDermott, J., White, D. R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., & O’Reilly, U.-M., (2012). Genetic programming needs better benchmarks. In Proceedings of the fourteenth international conference on genetic and evolutionary computation conference (pp. 791–798). New York, NY: ACM.
   Google Scholar
• Silva, S., & Almeida, J. (2003). GPLAB -- A genetic programming toolbox for MATLAB. In L. Gregersen (Ed.), Proceedings of the Nordic MATLAB Conference (pp. 273–278), Copenhagen, Denmark.
   Google Scholar
• 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, 141–179.
   Web of Science ®Google Scholar
• Spector, L. (2012). Assessment of problem modality by differential performance of lexicase selection in genetic programming: A preliminary report. In Proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion (pp. 401–408). Philadelphia, PA: ACM.
   Google Scholar
• Trawinski, B., Smetek, M., Telec, Z., & Lasota, T. (2012). Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. Applied Mathematics and Computer Science, 22, 867–881.
   Web of Science ®Google Scholar
• Trujillo, L., Spector, L., Naredo, E., & Martínez, Y. (2013, July 6--10). A behavior-based analysis of modal problems. In Genetic and evolutionary computation conference, GECCO’13 companion material proceedings (pp. 1047–1054). Amsterdam.
   Google Scholar
• Uy, N. Q., Hoai, N. X., O’Neill, M., Mckay, R. I., & Galv\’{a}n-L\’{o}pez, E. (2011). Semantically-based crossover in genetic programming: Application to real-valued symbolic regression. Genetic Programming and Evolvable Machines, 12, 91–119.
   Web of Science ®Google Scholar
• Zhang, M., & Smart, W. (2006). Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recognition Letters, 27, 1266–1274.
   Web of Science ®Google Scholar