Semantic Crossover Based on the Partial Derivative Error

  • Mario Graff
  • Ariel Graff-Guerrero
  • Jaime Cerda-Jacobo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)


There is great interest for the development of semantic genetic operators to improve the performance of genetic programming. Semantic genetic operators have traditionally been developed employing experimentally or theoretically-based approaches. Our current work proposes a novel semantic crossover developed amid the two traditional approaches. Our proposed semantic crossover operator is based on the use of the derivative of the error propagated through the tree. This process decides the crossing point of the second parent. The results show that our procedure improves the performance of genetic programming on rational symbolic regression problems.


Semantic Crossover Symbolic Regression 


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  1. 1.
    Blickle, T., Thiele, L.: Genetic programming and redundancy. Choice 1000, 2 (1994)Google Scholar
  2. 2.
    Nguyen, Q.U., Nguyen, X.H., O’Neill, M.: Semantic aware crossover for genetic programming: The case for real-valued function regression. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 292–302. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Uy, N.Q., Hoai, N.X., ONeill, M., McKay, R.I., Galvn-López, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genetic Programming and Evolvable Machines 12(2), 91–119 (2010)CrossRefGoogle Scholar
  4. 4.
    Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence), pp. 111–116 (2008)Google Scholar
  5. 5.
    Beadle, L., Johnson, C.: Semantically driven mutation in genetic programming. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1336–1342 (2009)Google Scholar
  6. 6.
    Beadle, L., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genetic Programming and Evolvable Machines 10(3), 307–337 (2009)CrossRefGoogle Scholar
  7. 7.
    Krawiec, K., Lichocki, P.: Approximating geometric crossover in semantic space. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 987–994. ACM, New York (2009)Google Scholar
  8. 8.
    Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 205–216. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Rojas, R.: Neural Networks: A Systematic Introduction, 1st edn. Springer (July 1996)Google Scholar
  11. 11.
    Topchy, A., Punch, W.F.: Faster genetic programming based on local gradient search of numeric leaf values. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 155–162 (2001)Google Scholar
  12. 12.
    Smart, W., Zhang, M.: Continuously evolving programs in genetic programming using gradient descent. In: Proceedings of 2004 Asia-Pacific Workshop on Genetic Programming (2004)Google Scholar
  13. 13.
    Zhang, M., Smart, W.: Genetic programming with gradient descent search for multiclass object classification. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 399–408. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Graff, M., Pena, R., Medina, A.: Wind speed forecasting using genetic programming. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 408–415 (2013)Google Scholar
  15. 15.
    Igel, C., Hüsken, M.: Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50, 105–123 (2003)CrossRefzbMATHGoogle Scholar
  16. 16.
    Poli, R.: TinyGP. See Genetic and Evolutionary Computation Conference (GECCO 2004) (June 2004), competition at
  17. 17.
    Nissen, S.: Implementation of a fast artificial neural network library (fann). Technical report, Department of Computer Science University of Copenhagen, DIKU (2003),
  18. 18.
    Graff, M., Poli, R.: Practical performance models of algorithms in evolutionary program induction and other domains. Artificial Intelligence 174(15), 1254–1276 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mario Graff
    • 1
  • Ariel Graff-Guerrero
    • 2
  • Jaime Cerda-Jacobo
    • 1
  1. 1.Division de Estudios de Posgrado  , Facultad de Ingenieria EléctricaUniversidad Michoacana de San Nicolás de HidalgoMéxico
  2. 2.PET CentreCentre for Addiction and Mental HealthTorontoCanada

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