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Cross-task code reuse in genetic programming applied to visual learning Cover

Cross-task code reuse in genetic programming applied to visual learning

International Journal of Applied Mathematics and Computer Science
Volume 24 (2014): Issue 1 (March 2014)
By: ,   and    
Open Access
|Mar 2014
References

References

  1. Bhanu, B., Lin, Y. and Krawiec, K. (2005)., Springer-Verlag, New York, NY.
    Search in Google Scholar
  2. Caruana, R. (1997). Multitask learning,(1): 41-75.
    Open DOISearch in Google Scholar
  3. Chang, Y.F., Lee, J.C., Mohd Rijal, O. and Syed Abu Bakar, S.A.R. (2010). Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model,(4): 727-738, DOI: 10.2478/v10006-010-0055-x.
    Open DOISearch in Google Scholar
  4. Ciresan, D.C., Meier, U. and Schmidhuber, J. (2012). Multi-column deep neural networks for image classification,, pp. 3642-3649.
    Search in Google Scholar
  5. Fabijańska, A. (2012). A survey of subpixel edge detection methods for images of heat-emitting metal specimens,(3): 695-710, DOI: 10.2478/v10006-012-0052-3.
    Open DOISearch in Google Scholar
  6. Galvan Lopez, E., Poli, R. and Coello Coello, C.A. (2004). Reusing code in genetic programming,M. Keijzer, U.-M. O’Reilly, S.M. Lucas, E. Costa and T. Soule (Eds.),, Lecture Notes in Computer Science, Vol. 3003, Springer-Verlag, Berlin/Heidelberg, pp. 359-368.
    Open DOISearch in Google Scholar
  7. Ghosn, J. and Bengio, Y. (2003). Bias learning, knowledge sharing,(4): 748-765.
    Open DOISearch in Google Scholar
  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009). The Weka data mining software: An update,(1): 10-18.
    Open DOISearch in Google Scholar
  9. Haynes, T. (1997). On-line adaptation of search via knowledge reuse,J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba and R.L. Riolo (Eds.),, Morgan Kaufmann, San Francisco, CA, pp. 156-161.
    Search in Google Scholar
  10. Holland, J. (1975)., Vol. 1, University of Michigan Press, Ann Arbor, MI.
    Search in Google Scholar
  11. Hornby, G.S. and Pollack, J.B. (2002). Creating high-level components with a generative representation for body-brain evolution,(3): 223-246.
    Open DOISearch in Google Scholar
  12. Howard, D. (2003). Modularization by multi-run frequency driven subtree encapsulation,R.L. Riolo and B. Worzel (Eds.),, Kluwer, New York, NY, Chapter 10, pp. 155-172.
    Open DOISearch in Google Scholar
  13. Howard, D., Roberts, S.C. and Ryan, C. (2006). Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance,(11): 1275-1288.
    Open DOISearch in Google Scholar
  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,M. Keijzer (Ed.),, Association for Computing Machinery, Seattle, WA.
    Search in Google Scholar
  15. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007a). Genetic programming for cross-task knowledge sharing,D. Thierens (Ed.),, Association for Computing Machinery, London, pp. 1620-1627.
    Open DOISearch in Google Scholar
  16. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007b). Knowledge reuse in genetic programming applied to visual learning,D. Thierens (Ed.),, Association for Computing Machinery, London, pp. 1790-1797.
    Open DOISearch in Google Scholar
  17. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2007c). Learning and recognition of hand-drawn shapes using generative genetic programming,M. Giacobini (Ed.),, Lecture Notes in Computer Science, Vol. 4448, Springer-Verlag, Berlin/Heidelberg, pp. 281-290.
    Search in Google Scholar
  18. Koza, J. (1992)., MIT Press, Cambridge, MA.
    Search in Google Scholar
  19. Koza, J.R. (1994)., MIT Press, Cambridge, MA.
    Search in Google Scholar
  20. Koza, J.R., Bennett III, F.H., Andre, D. and Keane, M.A. (1996). Reuse, parameterized reuse, and hierarchical reuse of substructures in evolving electrical circuits using genetic programming,T. Higuchi (Ed.),, Lecture Notes in Computer Science, Vol. 1259, Springer-Verlag, Berlin.
    Search in Google Scholar
  21. Krawiec, K. (2006). Learning high-level visual concepts using attributed primitives and genetic programming,F. Rothlauf (Ed.),, Lecture Notes in Computer Science, Vol. 3907, Springer-Verlag, Berlin/Heidelberg, pp. 515-519.
    Open DOISearch in Google Scholar
  22. Krawiec, K. (2007). Generative learning of visual concepts using multiobjective genetic programming,(16): 2385-2400.
    Open DOISearch in Google Scholar
  23. Krawiec, K. and Bhanu, B. (2005). Visual learning by coevolutionary feature synthesis,(3): 409-425.
    Open DOISearch in Google Scholar
  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,(1): 121-134.
    Open DOISearch in Google Scholar
  25. Langdon, W.B. and Poli, R. (2002)., Springer-Verlag, New York, NY.
    Open DOISearch in Google Scholar
  26. Li, B., Li, X., Mabu, S. and Hirasawa, K. (2012). Towards automatic discovery and reuse of subroutines in variable size genetic network programming,X. Li (Ed.),, pp. 485-492.
    Search in Google Scholar
  27. Louis, S. and McDonnell, J. (2004). Learning with case-injected genetic algorithms,(4): 316-328.
    Open DOISearch in Google Scholar
  28. Luke, S. (2002). ECJ evolutionary computation system, http://cs.gmu.edu/eclab/projects/ecj/.
    Search in Google Scholar
  29. Mitchell, T.M. (2006). The discipline of machine learning,, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA.
    Search in Google Scholar
  30. Montana, D.J. (1993). Strongly typed genetic programming,, Bolt Beranek and Newman, Inc., Cambridge, MA.
    Search in Google Scholar
  31. Moya, M.R., Koch, M.W. and Hostetler, L.D. (1993). One-class classifier networks for target recognition applications,, pp. 797-801.
    Search in Google Scholar
  32. O’Sullivan, J. and Thrun, S. (1995)., Carnegie Mellon University, School of Computer Science, Pittsburgh, PA.
    Search in Google Scholar
  33. Poli, R., Langdon, W.B. and McPhee I.N.F. (2008). A field guide to genetic programming, http://www.gp-field-guide.org.uk/.
    Search in Google Scholar
  34. Perez, C.B. and Olague, G. (2013). Genetic programming as strategy for learning image descriptor operators,(4): 561-583.
    Open DOISearch in Google Scholar
  35. Pratt, L.Y., Mostow, J. and Kamm, C.A. (1991). Direct transfer of learned information among neural networks,, pp. 584-589.
    Search in Google Scholar
  36. Rizki, M.M., Zmuda, M.A. and Tamburino, L.A. (2002). Evolving pattern recognition systems,(6): 594-609.
    Open DOISearch in Google Scholar
  37. Roberts, S.C., Howard, D. and Koza, J.R. (2001). Evolving modules in genetic programming by subtree encapsulation,J.F. Miller (Ed.),, Lecture Notes in Computer Science, Vol. 2038, Springer-Verlag, Berlin, pp. 160-175.
    Open DOISearch in Google Scholar
  38. Rosca, J.P. and Ballard, D.H. (1996). Discovery of subroutines in genetic programming,P.J.Angeline and K.E.Kinnear, Jr. (Eds.),,MIT Press, Cambridge, MA, Chapter 9, pp. 177-202.
    Search in Google Scholar
  39. Seront, G. (1995). External concepts reuse in genetic programming,E.V. Siegel and J.R. Koza (Eds.),, AAAI/MIT, Cambridge, MA, pp. 94-98.
    Search in Google Scholar
  40. Tackett,W.A. (1993). Genetic generation of “dendritic” trees for image classification,, pp. IV 646-649.
    Search in Google Scholar
  41. Teller, A. and Veloso, M. (1997). PADO: A new learning architecture for object recognition,K. Ikeuchi and M. Veloso (Eds.),, Oxford Press, New York, NY, pp. 77-112.
    Search in Google Scholar
  42. Trujillo, L. and Olague, G. (2006). Synthesis of interest point detectors through genetic programming,M. Cattolico (Ed.),, Association for Computing Machinery, Seattle, WA, pp. 887-894.
    Open DOISearch in Google Scholar
  43. Vilalta, R. and Drissi, Y. (2002). A perspective view and survey of meta-learning,(2): 77-95.
    Open DOISearch in Google Scholar
  44. Whitley, D., Rana, S. and Heckendorn, R. (1999). The island model genetic algorithm: On separability, population size and convergence,(1): 33-47.
    Search in Google Scholar
DOI: https://doi.org/10.2478/amcs-2014-0014 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 183 - 197
Published on: Mar 25, 2014
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
genetic programming,
code reuse,
knowledge sharing,
visual learning,
multi-task learning,
optical character recognition.
Related subjects:
Mathematics,
Applied mathematics

© 2014 Wojciech Jaśkowski, Krzysztof Krawiec, Bartosz Wieloch, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

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