A Novel Genetic Programming Based Classifier Design Using a New Constructive Crossover Operator with a Local Search Technique

  • Arpit Bhardwaj
  • Aruna Tiwari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7995)


A common problem in genetic programming search algorithms is the destructive nature of the crossover operator in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. In this paper, we proposed the integration of variants of local search techniques in the breeding process, done by allowing parents to produce many off springs and applying a selection procedure to choose high performing off springs. Our approach has removed the randomness of crossover operator. To demonstrate our approach, we designed a Multiclass classifier and tested it on various benchmark datasets. Our method has shown the tremendous improvement over the other state of the art methods.


Genetic Programming Crossover Local Search Technique 


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  1. 1.
    Koza, J.R.: Genetic Programming — On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  2. 2.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming - An Introduction. Morgan Kaufmann, San Mateo (1998)zbMATHCrossRefGoogle Scholar
  3. 3.
    Blickle, T., Thiele, L.: A Mathematical Analysis of Tournament Selection. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 9–16 (1995)Google Scholar
  4. 4.
    Nordin, P., Banzhaf, W.: Complexity compression and evolution. In: Eshelman, L. (ed.) Genetic Algorithms: Proceedings of the Sixth International Conference, Pittsburgh, PA, USA, July 15-19, pp. 310–317. Morgan Kaufmann (1995)Google Scholar
  5. 5.
    Nordin, P., Francone, F., Banzhaf, W.: Explicitly Defined Introns and Destructive Crossover in Genetic Programming. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 6–22 (1995)Google Scholar
  6. 6.
    Tackett, W.A.: Recombination, Selection, and the Genetic Construction of Computer Programs. PhD thesis, University of Southern California, Los Angeles, CA, USA (1994)Google Scholar
  7. 7.
    Purohit, A., Bhardwaj, A., Tiwari, A., Choudhari, N.S.: Removing Code Bloating in Crossover Operation in Genetic Programming. In: IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011, June 3-5 (2011); 978-1-4577-0590-8/11/$26.00 ©2011 IEEE MIT, Anna University, ChennaiGoogle Scholar
  8. 8.
    Lang, K.J.: Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis of Koza’s. In: Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann (1995)Google Scholar
  9. 9.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, Department of Information and Computer Science, Irvine, CA (2007),
  10. 10.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. On the Automatic Evalution of Computer Programs and Its Application. Morgan Kaufmann, San Mateo (1998)Google Scholar
  11. 11.
    Rich, E., Knight, K., Nair, S.B.: Artificial Intelligence. Tata Mc-Graw-Hill (2009) ISBN: 0070087709Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arpit Bhardwaj
    • 1
  • Aruna Tiwari
    • 1
  1. 1.Computer Science DepartmentIndian Institute of TechnologyIndoreIndia

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