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European Conference on Genetic Programming

EuroGP 2014: Genetic Programming pp 37–47Cite as

Semantic Crossover Based on the Partial Derivative Error

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8599))

Abstract

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.

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References

  1. Blickle, T., Thiele, L.: Genetic programming and redundancy. Choice 1000, 2 (1994)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Beadle, L., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genetic Programming and Evolvable Machines 10(3), 307–337 (2009)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  10. Rojas, R.: Neural Networks: A Systematic Introduction, 1st edn. Springer (July 1996)

    Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  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. Igel, C., Hüsken, M.: Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50, 105–123 (2003)

    Article  MATH  Google Scholar 

  16. Poli, R.: TinyGP. See Genetic and Evolutionary Computation Conference (GECCO 2004) (June 2004), competition at http://cswww.essex.ac.uk/staff/sml/gecco/TinyGP.html

  17. Nissen, S.: Implementation of a fast artificial neural network library (fann). Technical report, Department of Computer Science University of Copenhagen, DIKU (2003), http://fann.sf.net

  18. Graff, M., Poli, R.: Practical performance models of algorithms in evolutionary program induction and other domains. Artificial Intelligence 174(15), 1254–1276 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  19. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80 (1945)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Division de Estudios de Posgrado  , Facultad de Ingenieria Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, México

    Mario Graff & Jaime Cerda-Jacobo

  2. PET Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada

    Ariel Graff-Guerrero

Authors
  1. Mario Graff
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  2. Ariel Graff-Guerrero
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  3. Jaime Cerda-Jacobo
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Editor information

Editors and Affiliations

  1. University College Dublin, UCD CASL, Belfield, Dublin 4, Ireland

    Miguel Nicolau

  2. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  3. Faculty of Computer Science, Dalhousie University, University Avenue 6050, B3H 1W5, Halifax, NS, Canada

    Malcolm I. Heywood

  4. Instituto Superior de Estatística e Gestão de Informaticão, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Mauro Castelli

  5. Departamento de Arquitectura y Tecnología de Computadores (ETS), Universidad de Granada, 18071, Granada, Spain

    Pablo García-Sánchez  & Juan J. Merelo  & 

  6. Departamento de Informática, Universidad de Jaén, 23071, Jaén, Spain

    Victor M. Rivas Santos

  7. Institute for Informatics and Digital Innovation, Edinburgh Napier University, Merchiston Campus, EH10 5DT, Edinburgh, UK

    Kevin Sim

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© 2014 Springer-Verlag Berlin Heidelberg

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Graff, M., Graff-Guerrero, A., Cerda-Jacobo, J. (2014). Semantic Crossover Based on the Partial Derivative Error. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_4

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  • DOI: https://doi.org/10.1007/978-3-662-44303-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44302-6

  • Online ISBN: 978-3-662-44303-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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