Abstract
This paper describes an approach to the use of gradient descent search in tree based genetic programming for object recognition problems. A weight parameter is introduced to each link between two nodes in a program tree. The weight is defined as a floating point number and determines the degree of contribution of the sub-program tree under the link with the weight. Changing a weight corresponds to changing the effect of the sub-program tree. The weight changes are learnt by gradient descent search at a particular generation. The programs are evolved and learned by both the genetic beam search and the gradient descent search. This approach is examined and compared with the basic genetic programming approach without gradient descent on three object classification problems of varying difficulty. The results suggest that the new approach works well on these problems.
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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, Seoul, Korea, vol. 2, pp. 1070–1077. IEEE Press, Los Alamitos (2001)
Song, A., Ciesielski, V., Williams, H.: Texture classifiers generated by genetic programming. In: 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: Proceedings of the 5th International Conference on Genetic Algorithms, ICGA 1993, University of Illinois at Urbana-Champaign, pp. 303–309. Morgan Kaufmann, San Francisco (1993)
Winkeler, J.F., Manjunath, B.S.: Genetic programming for object detection. In: Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, 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)
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. Foundations, vol. 1. The MIT Press, Cambridge (1986)
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)
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. Morgan Kaufmann Publishers, San Francisco (1998)
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, 841–859 (2003)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, Florida (1994)
Poli, R.: Discovery of symbolic, neuro-symbolic and neural networks with parallel distributed genetic programming. In: Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA 1997, Norwich, UK. Springer, Heidelberg (1997)
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Zhang, M., Smart, W. (2005). Learning Weights in Genetic Programs Using Gradient Descent for Object Recognition. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_42
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DOI: https://doi.org/10.1007/978-3-540-32003-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
Online ISBN: 978-3-540-32003-6
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