Construction of image feature extractors based on multi-objective genetic programming with redundancy regulations
Inhaltsverzeichnis
Reference
Ukrit Watchareeruetai and Tetsuya Matsumoto and Yoshinori Takeuchi and Hiroaki Kudo and Noboru Ohnishi: Construction of image feature extractors based on multi-objective genetic programming with redundancy regulations. IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009, pp. 1328-1333, October 11-14 2009.
DOI
http://dx.doi.org/10.1109/ICSMC.2009.5346242
Abstract
This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity. Experimental results exhibit that the proposed MOGP-based FEPs construction system provides obviously better performance than the original non-dominated sorting approach.
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Used References
A. Abraham, L. Jain, and R. Goldberg (Eds.), Evolutionary Multiobjective Optimization: Theoretical Advances and Application, Springer-Verlag, USA, 2005.
A. Teller and M. Veloso, "PADO: a new learning architecture for object recognition," in Symbolic Visual Learning, K. Ikeuchi and M. Veloso, Eds, Oxford, U.K.: Oxford Univ. Press, pp. 77-112, 1997.
E. Zizler and L. Thile, "Multiobjective optimization using evolutionary algorithms: a comparative study," in Parallel Problem Solving from Nature V, A.E. Eiben, Ed, Amsterdam, pp. 292-301, 1998.
J.M. Daida, H. Li, R. Tang, and A.M. Hilss, "What makes a problem GPhard? validating a hypothesis of structural causes", in Proc. of GECCO-2003, LNCS 2724, Springer-Verlag, pp.1665-16677, 2003.
J. Niehaus, C. Igel, and W. Banzhaf, "Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database," Evolutionary Computation, vol. 15, no. 2, pp. 199-221, 2007. http://dx.doi.org/10.1162/evco.2007.15.2.199
K.A. De Jong, Evolutionary Computation: A Unified Approach, The MIT Press, 2006.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Trans. Evolutionary Computation, vol.6, no.2, pp.182-197, April 2002. http://dx.doi.org/10.1109/4235.996017
K. Krawiec and B. Bhanu, "Visual learning by evolutionary and coevolutionary feature synthesis," IEEE Transactions on Evolutionary Computation, vol. 11, no. 5, pp. 635-650, 2007. http://dx.doi.org/10.1109/TEVC.2006.887351
L. Trujillo and G. Olague, "Automated design of image operators that detect interest points," Evolutionary Computation, vol. 16, no. 4, MIT Press, pp. 483-507, 2008. http://dx.doi.org/10.1162/evco.2008.16.4.483
M. Brameier and W. Banzhaf, Linear Genetic Programming, New York, Springer-Verlag, 2007.
M. Zhang, V. Ciesielski, and P. Andreae, "A domain-independent window approach to multiclass object detection using genetic programming," EURASIP Journal on Applied Signal Processing, vol.8, pp. 841-859, 2003. http://dx.doi.org/10.1155/S1110865703303063
N. Srinivas, and K. Deb, "Multiobjective function optimization using nondominated sorting genetic algorithms", Evolutionary Computation, vol.2, no.3, pp.221-248, MIT Press, 1995. http://dx.doi.org/10.1162/evco.1994.2.3.221
R. Poli, W.B. Langdon, and N.F. McPhee, A Filed Guide to Genetic Programming, Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008)
S. Shirakawa and T. Nagao, "Genetic image network (GIN): automatically construction of image processing," in Proc. IWAIT-2007, Bangkok, Thailand, pp. 643-648, 2007.
S. Theodoridis and K. Koutroumbas, Pattern Recognition 2 nd edition, Academic Press, 2003.
U. Watchareeruetai, Y. Takeuchi, T. Matsumoto, H. Kudo, and N. Ohnishi, "Computer vision based method for detecting weeds in lawns", Machine Vision and Applications, vol.17, no.5, pp.287-296, 2006. http://dx.doi.org/10.1007/s00138-006-0039-x
U. Watchareeruetai, Y. Takeuchi, T. Matsumoto, H. Kudo, and N. Ohnishi, "Transformation of redundant representations of linear genetic programming into canonical forms for efficient extraction of image features", in Proc. IEEE Congress on Evolutionary Computation (CEC-08), Hong Kong, China, pp.1996-2003, 2008. http://dx.doi.org/10.1109/CEC.2008.4631062
U. Watchareeruetai, Y. Takeuchi, T. Matsumoto, H. Kudo, and N. Ohnishi, "Efficient construction of feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", in Foundation of Computational Intelligence Volume 4: Bio-inspired Data Mining, A. Abraham et al., Eds, Springer-Verlag, pp.355-375, 2009.
W.M. Spears and K.A. De Jong, "On the virtues of parameterized uniform crossover," in Proc. ICGA-91, R.K. Belew and L.B. Booker, Eds, Morgan Kaufmann, pp. 230-236, 1991.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Berlin, Germany, Springer-Verlag, 1996.
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