Advertisement

Promoting generalisation of learned behaviours in genetic programming

  • Ibrahim Kuscu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

Recently, growing numbers of research concentrate on robustness of the programs evolved using Genetic Programming (GP). While some of the researchers report on the brittleness of the solutions evolved, some others proposed methods of promoting robustness. It is important that these methods are not ad hoc and specific for a certain experimental setup. In this research, brittleness of solutions found for the artificial ant problem is reported and a new method promoting generalisation of the solutions in GP is presented.

Keywords

Genetic programming learning robustness generalisation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hilan Bensusan and Ibrahim Kuscu. Constructive induction using genetic programming. In ICML'96, Evolutionary computing and Machine Learning Workshop, 1996.Google Scholar
  2. 2.
    Tommaso F. Bersano-Begey and Jason M. Daida. A discussion on generality and robustness and a framework for fitness set construction in genetic programming to promote robustness. In John R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, pages 11–18, Stanford University, CA, USA, 13–16 July 1997. Stanford Bookstore.Google Scholar
  3. 3.
    Frank D. Francone, Peter Nordin, and Wolfgang Banzhaf. Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 72–80, Stanford University, CA, USA, July 1996. MIT Press.Google Scholar
  4. 4.
    Thomas D. Haynes and Roger L. Wainwright. A simulation of adaptive agents in hostile environment. In K. M. George, Janice H. Carroll, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Proceedings of the 1995 ACM Symposium on Applied Computing, pages 318–323, Nashville, USA, 1995. ACM Press.Google Scholar
  5. 5.
    Dale Hooper and Nicholas S. Flann. Improving the accuracy and robustness of genetic programming through expression simplification. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, page 428, Stanford University, CA, USA, 28–31 July 1996. MIT Press.Google Scholar
  6. 6.
    Takuya Ito, Hitoshi Iba, and Masayuki Kimura. Robustness of robot programs generated by genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 28–31 July 1996. MIT Press. 321–326.Google Scholar
  7. 7.
    D. Jefferson, et al. Evolution as a theme in artificial life: The genesys/tracker system. In Artificial Life II. Addison-Wesley, 1991.Google Scholar
  8. 8.
    M. Kearns and U. Vazirani. An Introduction to Computational Learning Theory. MIT Press, Cambridge, Massachussets, USA, 1994.Google Scholar
  9. 9.
    John Koza. Genetic Programming:On the programming of computers by means of natural selection. MIT press, Cambridge, MA, 1992.Google Scholar
  10. 10.
    John Koza. Genetic Programming II. MIT press, 1994.Google Scholar
  11. 11.
    I. Kuscu. Evolving a generalised behaviour: Artificial ant problem revisited. In The Seventh Annual Conference on Evolutionary Programming, Forthcoming 1998.Google Scholar
  12. 12.
    Ibrahim Kuscu. Evolution of learning rules for hard learning problems. In Lawrence J. Fogel, Peter J. Angeline, and T Baeck, editors, Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming. MIT Press, 1996.Google Scholar
  13. 13.
    F. W. Moore and O. N. Garcia. New methodology for reducing brittleness in genetic programming. In E. Pohl, editor, Proceedings of the National Aerospace and Electronics 1997 Conference (NAECON-97). IEEE Press, 1997.Google Scholar
  14. 14.
    Peter Nordin and Wolfgang Banzhaf. Genetic programming controlling a miniature robot. In E. V. Siegel and J. R. Koza, editors, Working Notes for the AAAI Symposium on Genetic Programming, pages 61–67, MIT, Cambridge, MA, USA, 10–12 November 1995. AAAI.Google Scholar
  15. 15.
    Craig W. Reynolds. An evolved, vision-based behavioral model of obstacle avoidance behaviour. In Christopher G. Langton, editor, Artificial Life III, volume XVII of SFI Studies in the Sciences of Complexity, pages 327–346. Addison-Wesley, Santa Fe Institute, New Mexico, USA, 15–19 June 1992 1994.Google Scholar
  16. 16.
    Justinian Rosca. Generality versus size in genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 381–387, Stanford University, CA, USA, 1996. MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Ibrahim Kuscu
    • 1
  1. 1.Cognitive and Computing SciencesUniversity of SussexBrightonUK

Personalised recommendations