Abstract
This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems. Gradient descent search is introduced to the GP mechanism and is embedded into the genetic beam search, which allows the evolutionary learning process to globally follow the beam search and locally follow the gradient descent search. Two different methods, an online gradient descent scheme and an offline gradient descent scheme, are developed and compared with the basic GP method on three image data sets with object classification problems of increasing difficulty. The results suggest that both the online and the offline gradient descent GP methods outperform the basic GP method in terms of both classification accuracy and training efficiency and that the online scheme achieved better performance than the offline scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andre, D.: Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 477–494. MIT Press, Cambridge (1994)
Howard, D., Roberts, S.C., Brankin, R.: Target detection in SAR imagery by genetic programming. Advances in Engineering Software 30, 303–311 (1999)
Loveard, T., Ciesielski, V.: Representing classification problems in genetic programming. In: Proceedings of the Congress on Evolutionary Computation, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, May 27-30, vol. 2, pp. 1070–1077. IEEE Press, Los Alamitos (2001)
Song, A., Ciesielski, V., Williams, H.: Texture classifiers generated by genetic programming. In: Fogel, D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow, P., Shackleton, M. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 243–248. IEEE Press, Los Alamitos (2002)
Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA 1993, University of Illinois at Urbana-Champaign, July 17-21, pp. 303–309. Morgan Kaufmann, San Francisco (1993)
Winkeler, J.F., Manjunath, B.S.: Genetic programming for object detection. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July 13-16, pp. 330–335. Morgan Kaufmann, San Francisco (1997)
Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N.Y. (ed.) AI 1999. LNCS(LNAI), vol. 1747, pp. 180–192. Springer, Heidelberg (1999)
Loveard, T.: Genetic programming with meta-search: Searching for a successful population within the classification domain. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 119–129. Springer, Heidelberg (2003)
Ryan, C., Keijzer, M.: An analysis of diversity of constants of genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 404–413. Springer, Heidelberg (2003)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. In: Subject: Genetic programming (Computer science), Morgan Kaufmann Publishers/Dpunkt-verlag, San Francisco/Heidelburg (1998) ISBN: 1-55860-510-X
Zhang, M., Ciesielski, V., Andreae, P.: A domain independent window-approach to multiclass object detection using genetic programming. EURASIP Journal on Signal Processing, Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis 2003(8), 841–859 (2003)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, M., Smart, W. (2004). Genetic Programming with Gradient Descent Search for Multiclass Object Classification. In: Keijzer, M., O’Reilly, UM., Lucas, S., Costa, E., Soule, T. (eds) Genetic Programming. EuroGP 2004. Lecture Notes in Computer Science, vol 3003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24650-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-24650-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21346-8
Online ISBN: 978-3-540-24650-3
eBook Packages: Springer Book Archive