Skip to main content
SpringerLink
Log in

Using Loops in Genetic Programming for a Two Class Binary Image Classification Problem

  • Conference paper
AI 2004: Advances in Artificial Intelligence (AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3339))

Included in the following conference series:

Abstract

Loops are rarely used in genetic programming (GP), because they lead to massive computation due to the increase in the size of the search space. We have investigated the use of loops with restricted semantics for a problem in which there are natural repetitive elements, that of distinguishing two classes of images. Using our formulation, programs with loops were successfully evolved and performed much better than programs without loops. Our results suggest that loops can successfully used in genetic programming in situations where domain knowledge is available to provide some restrictions on loop semantics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Langdon, W.B., Poli, R.: Why ants are hard. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, pp. 22–25. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  2. Koza, J.R., Bennet III, F.H., Andre, D., Keane, M.A.: Genetic Programming III; Darwinian invention and problem solving. Morgan Kaufmann, San Francisco (1999)

    MATH  Google Scholar 

  3. Kinnear Jr, K.E.: Generality and difficulty in genetic programming: Evolving a sort. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA 1993, University of Illinois at Urbana-Champaign, 17-21, pp. 287–294. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  4. Langdon, W.B.: Data structures and genetic programming. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming, vol. 2, pp. 395–414. MIT Press, Cambridge (1996)

    Google Scholar 

  5. Sidney Maxwell III, R.: Experiments with a coroutine model for genetic programming. In: Proceedings of the 1998 United Kingdom Automatic Control Council International Conference on Control (UKACC International Conference on Control 1998), University of Wales, Swansea, UK, vol. 455, pp. 1–4. IEEE Press, Los Alamitos (1998)

    Google Scholar 

  6. Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N. (ed.) AI 1999. LNCS, vol. 1747, pp. 180–192. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  7. Roberts, S.C., Howard, D.: Genetic programming for image analysis: Orientation detection. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, Nevada, USA, July 10-12, pp. 651–657. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  8. Ross, B.J., Gualtieri, A.G., Fueten, F., Budkewitsch, P.: Hyperspectral image analysis using genetic programming. In: Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, 9-13 July, pp. 1196–1203. Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  9. Ciesielski, V., Li, X.: Experiments with explicit for-loops in genetic programming. In: Proceedings of Congress on Evolutionary Computation (CEC 2004), June, pp. 494–501. IEEE Press, Los Alamitos (2004)

    Chapter  Google Scholar 

  10. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  11. Haynes, T.D., Schoenefeld, D.A.: Type inheritance in strongly typed genetic programming. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming, vol. 2, pp. 359–376. MIT Press, Cambridge (1996)

    Google Scholar 

  12. Langdon, W.B., Poli, R.: Fitness causes bloat: Mutation. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 37–48. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001

    Xiang Li & Vic Ciesielski

Authors
  1. Xiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vic Ciesielski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Monash University, VIC 3800, Australia

    Geoffrey I. Webb

  2. Science, Engineering and Technology Portfolio, Royal Melbourne Institute of Technology, VIC 3001, Melbourne, Australia

    Xinghuo Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, X., Ciesielski, V. (2004). Using Loops in Genetic Programming for a Two Class Binary Image Classification Problem. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_77

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30549-1_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24059-4

  • Online ISBN: 978-3-540-30549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation