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Warianty tytułu
Języki publikacji
Abstrakty
We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.
Rocznik
Tom
Strony
183--197
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
autor
- Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
autor
- Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
autor
- Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
Bibliografia
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- [12] Howard, D. (2003). Modularization by multi-run frequency driven subtree encapsulation, in R.L. Riolo and B. Worzel (Eds.), Genetic Programming Theory and Practice, Kluwer, New York, NY, Chapter 10, pp. 155–172.
- [13] Howard, D., Roberts, S.C. and Ryan, C. (2006). Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance, Pattern Recognition Letters 27(11): 1275–1288.
- [14] Hsu, W.H., Harmon, S.J., Rodriguez, E. and Zhong, C. (2004). Empirical comparison of incremental reuse strategies in genetic programming for keep-away soccer, in M. Keijzer (Ed.), Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Seattle, WA.
- [15] Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007a). Genetic programming for cross-task knowledge sharing, in D. Thierens (Ed.), Genetic and Evolutionary Computation Conference GECCO, Association for Computing Machinery, London, pp. 1620–1627.
- [16] Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007b). Knowledge reuse in genetic programming applied to visual learning, in D. Thierens (Ed.), Genetic and Evolutionary Computation Conference GECCO, Association for Computing Machinery, London, pp. 1790–1797.
- [17] Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007c). Learning and recognition of hand-drawn shapes using generative genetic programming, in M. Giacobini (Ed.), EvoWorkshops 2007, Lecture Notes in Computer Science, Vol. 4448, Springer-Verlag, Berlin/Heidelberg, pp. 281–290.
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- [21] Krawiec, K. (2006). Learning high-level visual concepts using attributed primitives and genetic programming, in F. Rothlauf (Ed.), EvoWorkshops 2006, Lecture Notes in Computer Science, Vol. 3907, Springer-Verlag, Berlin/Heidelberg, pp. 515–519.
- [22] Krawiec, K. (2007). Generative learning of visual concepts using multiobjective genetic programming, Pattern Recognition Letters 28(16): 2385–2400.
- [23] Krawiec, K. and Bhanu, B. (2005). Visual learning by coevolutionary feature synthesis, IEEE Transactions on System, Man, and Cybernetics, Part B 35(3): 409–425.
- [24] Kurashige, K., Fukuda, T. and Hoshino, H. (2003). Reusing primitive and acquired motion knowledge for gait generation of a six-legged robot using genetic programming, Journal of Intelligent and Robotic Systems 38(1): 121–134.
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- [42] Trujillo, L. and Olague, G. (2006). Synthesis of interest point detectors through genetic programming, in M. Cattolico (Ed.), Genetic and Evolutionary Computation Conference GECCO, Association for Computing Machinery, Seattle, WA, pp. 887–894.
- [43] Vilalta, R. and Drissi, Y. (2002). A perspective view and survey of meta-learning, Artificial Intelligence Review 18(2): 77–95.
- [44] Whitley, D., Rana, S. and Heckendorn, R. (1999). The island model genetic algorithm: On separability, population size and convergence, Journal of Computing and Information Technology 7(1): 33–47.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b46d733e-4e39-409b-a303-c62f87566fa1