Linear Genetic Programming for Multi-class Object Classification

  • Christopher Fogelberg
  • Mengjie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.


Genetic Programming Directed Acyclic Graph Shape Data Training Accuracy Linear Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Koza, J.R.: Genetic Programming. MIT Press, Campridge (1992)zbMATHGoogle Scholar
  2. 2.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming – An Introduction. In: On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco (1998)Google Scholar
  3. 3.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, London (1994)zbMATHGoogle Scholar
  4. 4.
    Loveard, T., Ciesielski, V.: Representing classification problems in genetic programming. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1070–1077. IEEE Press, Los Alamitos (2001)Google Scholar
  5. 5.
    Tackett, W.A.: Recombination, Selection, and the Genetic Construction of Computer Programs. PhD thesis, Faculty of the Graduate School, University of Southern C alifornia, Canoga Park, California, USA (1994)Google Scholar
  6. 6.
    Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Proceedings of the 12th Australian Joint Conference o n Artificial Intelligence. LNCS (LNAI), vol. 1747, pp. 180–192. Springer, Heidelberg (1999)Google Scholar
  7. 7.
    Zhang, M., Ciesielski, V., Andreae, P.: A domain independent window-approach to multiclass object detection using genetic programming. EURASIP Journal on Signal Processing 2003, 841–859 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Zhang, M., Smart, W.: Multiclass object classification using genetic programming. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 369–378. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Oltean, M., Grosan, C., Oltean, M.: Encoding multiple solutions in a linear genetic programming chromosome. In: Proceedings of 4th International Conference on Computational Science, Part III, pp. 1281–1288. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel distributed Processing, Explorations in the Microstructure of Cognition, Volume 1: Foundations. The MIT Press, Cambridge (1986)Google Scholar
  11. 11.
    Brameier, M., Banzhaf, W.: A comparison of genetic programming and neural networks in medical data analysis. Reihe CI 43/98, Dortmund University (1998)Google Scholar
  12. 12.
    Brameier, M., Banzhaf, W.: Effective linear genetic programming. Technical report, Department of Computer Science, University of Dortmund, Germany (2001)Google Scholar
  13. 13.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading (1989)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christopher Fogelberg
    • 1
  • Mengjie Zhang
    • 1
  1. 1.School of Mathematics, Statistics and Computer SciencesVictoria University of WellingtonWellingtonNew Zealand

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