Linear Genetic Programming for Multi-class Object Classification
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.
KeywordsGenetic Programming Directed Acyclic Graph Shape Data Training Accuracy Linear Genetic Programming
Unable to display preview. Download preview PDF.
- 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
- 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.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.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
- 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.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.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.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.Brameier, M., Banzhaf, W.: Effective linear genetic programming. Technical report, Department of Computer Science, University of Dortmund, Germany (2001)Google Scholar