Advertisement

Tree Adjoining Grammars, Language Bias, and Genetic Programming

  • Nguyen Xuan Hoai
  • R.I. McKay
  • H.A. Abbass
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)

Abstract

In this paper, we introduce a new grammar guided genetic programming system called tree-adjoining grammar guided genetic programming (TAG3P+), where tree-adjoining grammars (TAGs) are used as means to set language bias for genetic programming. We show that the capability of TAGs in handling context-sensitive information and categories can be useful to set a language bias that cannot be specified in grammar guided genetic programming. Moreover, we bias the genetic operators to preserve the language bias during the evolutionary process. The results pace the way towards a better understanding of the importance of bias in genetic programming.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Banzhaf W., Nordin P., Keller R.E., and Francone F.D.: Genetic Programming: An Introduction. Morgan Kaufmann Pub (1998).Google Scholar
  2. 2.
    Candito M. H. and Kahane S.: Can the TAG Derivation Tree Represent a Semantic Graph? An Answer in the Light of Meaning-Text Theory. In: Proceedings of TAG+4, Philadelphia, (1999) 25–28.Google Scholar
  3. 3.
    Cohen, W. W.: Grammatically Biased Learning: Learning Logic Programs Using an Explicit Antecedent Description Language. Technical Report, AT and Bell Laboratories, Murray Hill, NJ, (1993).Google Scholar
  4. 4.
    Gruau F.: On Using Syntactic Constraints with Genetic Programming. In: Advances in Genetic Programming II, The MIT Press, (1996) 377–394.Google Scholar
  5. 5.
    Geyer-Schulz A.: Fuzzy Rule-Based Expert Systems and Genetic Machine Learning. Physica-Verlag, Germany, (1995).Google Scholar
  6. 6.
    Hoai N. X.: Solving the Symbolic Regression Problem with Tree Adjunct Grammar Guided Genetic Programming: The Preliminary Result. In: the Proceedings of 5th Australasia-Japan Workshop in Evolutionary and Intelligent Systems, (2001) 52–61.Google Scholar
  7. 7.
    Hoai N. X., Mac Kay R. I., and Essam D.: Solving the Symbolic Regression Problem with Tree Adjunct Grammar Guided Genetic Programming. Australian Journal of Intelligent Information Processing Systems, 7(3), (2002) 114–121.Google Scholar
  8. 8.
    Hoai N.X., Y. Shan, and R. I. MacKay: Is Ambiguity is Useful or Problematic for Genetic Programming? A Case Study. To appear in: The Proceedings of 4th Asia-Pacific Conference on Evolutionary Computation and Simulated Learning (SEAL’02), (2002).Google Scholar
  9. 9.
    Joshi, A. K. and Schabes, Y.: Tree Adjoining Grammars. In: Handbook of Formal Languages, Rozenberg G. and Saloma A. (eds) Springer-Verlag, (1997) 69–123.Google Scholar
  10. 10.
    Joshi, A. K.. Levy, L. S., and Takahashi, M.: Tree Adjunct Grammars. Journal of Computer and System Sciences, 10 (1), (1975) 136–163.Google Scholar
  11. 11.
    Koza, J.: Genetic Programming, The MIT Press (1992).Google Scholar
  12. 12.
    Koza, J.: Genetic Programming II, The MIT Press (1994).Google Scholar
  13. 13.
    Mitchell T. M.: Machine Learning. McGraw-Hill, (1997).Google Scholar
  14. 14.
    Micthell T. M., Utgoff P., and BanerJi R.: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics. In: Machine Learning: An Artificial Intelligence Approach. Springer-Verlag, (1984) 163–190.Google Scholar
  15. 15.
    O’Neil M. and Ryan C.: Grammatical Evolution. IEEE Trans on Evolutionary Computation, 4 (4), (2000) 349–357.Google Scholar
  16. 16.
    Schabes Y.: Mathemantical and Computational Aspects of Lexicalized Grammars, Ph.D. Thesis, University of Pennsylvania, USA, (1990).Google Scholar
  17. 17.
    Shanker V.: A Study of Tree Adjoining Grammars. PhD. Thesis, University of Pennsylvania, USA, 1987.Google Scholar
  18. 18.
    Utgoff P.: Machine Learning of Inductive Bias. Kluwer Academic Publisher, (1986).Google Scholar
  19. 19.
    Weir D. J.: Characterizing Mildly Context-Sensitive Grammar Formalisms. PhD. Thesis, University of Pennsylvania, USA, 1988.Google Scholar
  20. 20.
    Valiant L.: A Theory of the Learnable. ACM, 27(11), (1984) 1134–1142.zbMATHCrossRefGoogle Scholar
  21. 21.
    Whigham P. A.: Search Bias, Language Bias and Genetic Programming. In: Genetic Programming 1996, The MIT Press, USA, (1996) 230–237.Google Scholar
  22. 22.
    Whigham P. A.: Grammatical Bias for Evolutionary Learning. Ph.D Thesis, University of New South Wales, Australia, (1996).Google Scholar
  23. 23.
    Wolpert D. and Macready W.: No Free Lunch Theorems for Search. Technical Report SFITR-95-02-010, Santa Fem, NM, 87501.Google Scholar
  24. 24.
    Wong M. L. and Leung K. S.: Evolutionary Program Induction Directed by Logic Grammars. Evolutionary Computation, 5 (1997) 143–180.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nguyen Xuan Hoai
    • 1
  • R.I. McKay
    • 1
  • H.A. Abbass
    • 1
  1. 1.School of Computer ScienceAustralian Defence Force AcademyAustralia

Personalised recommendations