Skip to main content
SpringerLink
Log in

Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators

  • Conference paper
Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

Included in the following conference series:

Abstract

In a series of papers [3-8], Daida et. al. highlighted the difficulties posed to Genetic Programming (GP) by the complexity of the structural search space, and attributed the problem to the expression tree representation in GP. In this paper, we show how to transform a fixed-arity expression tree in GP to a non fixed-arity tree (Catalan tree) using representation based on Tree Adjoining Grammars (TAGs). This non fixed-arity property, which is called feasibility, allows us to design many types of genetic operators (as in [16]). In particular, insertion/deletion operators arising naturally from the representation play a role as structural mutation operators. By using these dual operators on TAG-based representation, we demonstrate how these operators can help to soften the structural search difficulties in GP.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann Pub, San Francisco (1998)

    MATH  Google Scholar 

  2. Daida, J.M., Ross, S.J., McClain, J.J., Ampy, D.S., Holczer, M.: Challenges with Verification, Repeatability, and Meaningful Comparisons in Genetic Programming. In: Koza, J., et al. (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, pp. 64–69. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  3. Daida, J.M., Bertram, J.A., Polito, J.A., Stanhope, S.A.: Analysis of Single-Node (Building) Blocks in Genetic Programming. In: Spector, L., Langdon, W.B., O’Reilly, Angeline, P.J. (eds.) Advances in Genetic Programming 3, pp. 217–241. The MIT Press, Cambridge (1999)

    Google Scholar 

  4. Chaudri, O.A., et al.: Characterizing a Tunably Difficult Problem in Genetic Programming. In: Witley, L.D., et al. (eds.) Proceedings of GECCO 2000, pp. 395–402. Morgan Kaufmann Publisher, San Francisco (2000)

    Google Scholar 

  5. Daida, J.M., Polito, J.A., Stanhope, S.A., Bertram, R.R., Khoo, J.C., Chaudhary, S.A., Chaudhri, O.: What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming. Journal of Genetic Programming and Evolvable Machines 2, 165–191 (2001)

    Article  MATH  Google Scholar 

  6. Daida, J.M.: Limit to Expression in Genetic Programming: Lattice-Aggregate Modeling. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 273–278. IEEE Press, Los Alamitos (2002)

    Google Scholar 

  7. Daida, J.M., Hilss, A.M.: Identifying Structural Mechanism in Standard GP. In: Proceedings of GECCO 2003. LNCS, pp. 1639–1651. Springer, Heidelberg (2003)

    Google Scholar 

  8. Daida, J.M., Li, H., Tang, R., Hilss, A.M.: What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes. In: Proceedings of GECCO 2003. LNCS, pp. 1665–1677. Springer, Heidelberg (2003)

    Google Scholar 

  9. Gruau, F.: On Using Syntactic Constraints with Genetic Programming. In: Advances in Genetic Programming II, pp. 377–394. The MIT Press, Cambridge (1996)

    Google Scholar 

  10. Geyer-Schulz, A.: Fuzzy Rule-Based Expert Systems and Genetic Machine Learning. Physica-Verlag, Germany (1995)

    Google Scholar 

  11. Joshi, A.K., Levy, L.S., Takahashi, M.: Tree Adjunct Grammars. Journal of Computer and System Sciences 10(1), 136–163 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  12. Joshi, A.K., Schabes, Y.: Tree Adjoining Grammars. In: Rozenberg, G., Saloma, A. (eds.) Handbook of Formal Languages, pp. 69–123. Springer, Heidelberg (1997)

    Google Scholar 

  13. Koza, J.: Genetic Programming. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  14. Hoai, N.X., McKay, R.I.: A Framework for Tree Adjunct Grammar Guided Genetic Programming. In: Abbass, H.A., Barlow, M. (eds.) Proceedings of Post Graduate ADFA Conference on Computer Science (PACCS 2001), pp. 93–99 (2001)

    Google Scholar 

  15. Hoai, N.X., McKay, R.I., Essam, D., Chau, R.: Solving the Symbolic Regression Problem with Tree Adjunct Grammar Guided Genetic Programming: The Comparative Result. In: Proceedings of Congress on Evolutionary Computation (CEC’2002), pp. 1326–1331 (2002)

    Google Scholar 

  16. Hoai, N.X., McKay, R.I., Abbass, H.A.: Tree Adjoining Grammars, Language Bias, and Genetic Programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 335–344. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  17. Hoai, N.X., McKay, R.I.: An Investigation on the Roles of Insertion and Deletion Operators in Tree Adjoining Grammar Guided Genetic Programming. To appear in The Proceedings of Congress on Evolutionary Computation, CEC 2004 (2004)

    Google Scholar 

  18. O’Neil, M., Ryan, C.: Grammatical Evolution. IEEE Trans on Evolutionary Computation 4(4), 349–357 (2000)

    Google Scholar 

  19. Schabes, Y., Shieber, S.: An Alternative Conception of Tree-Adjoining Derivation. Computational Linguistics 20(1), 91–124 (1994)

    Google Scholar 

  20. Schabes, Y., Waters, R.C.: Tree Insertion Grammar: A Cubic-Time Parsable Formalism that Lexicalizes Context-Free Grammar without Changing the Trees Produced. Computational Linguistics 21(4), 479–514 (1995)

    MathSciNet  Google Scholar 

  21. Schabes, Y.: Mathemantical and Computational Aspects of Lexicalized Grammars, Ph.D. Thesis, University of Pennsylvania, USA (1990)

    Google Scholar 

  22. Sedgewick, R., Flajolet, P.: An Introduction to the Analysis of Algorithms. Addison-Wesley, Reading (1996)

    MATH  Google Scholar 

  23. Shanker, V.: A Study of Tree Adjoining Grammars. PhD. Thesis, University of Pennsylvania, USA (1987)

    Google Scholar 

  24. Vanneschi, L., Tomassini, M., Collard, P., Clergue, M.: Fitness Distance Correlation in Structural Mutation Genetic Programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 455–464. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  25. Vanneschi, L., Tomassini, M., Collard, P., Clergue, M.: Fitness Distance Correlation in Genetic Programming: a Constructive Counterexample. In: Proceedings of Congress on Evolutionary Computation (CEC’2003), pp. 289–296. IEEE Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  26. Weir, D.J.: Characterizing Mildly Context-Sensitive Grammar Formalisms. PhD. Thesis, University of Pennsylvania, USA, (1988)

    Google Scholar 

  27. Whigham, P.A.: Grammatical Bias for Evolutionary Learning. Ph.D Thesis, University of New South Wales, Australia (1996)

    Google Scholar 

  28. Whigham, P.A.: Grammatically-based Genetic Programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 33–41. Morgan Kaufmann Pub., San Francisco (1995)

    Google Scholar 

  29. Whigham, P.A.: Search Bias, Language Bias and Genetic Programming. In: Genetic Programming 1996, pp. 230–237. The MIT Press, USA (1996)

    Google Scholar 

  30. Wong, M.L., Leung, K.S.: Evolutionary Program Induction Directed by Logic Grammars. Evolutionary Computation 5, 143–180 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of IT & EE, Australian Defence Force academy, University of New South Wales, ACT 2600, Australia

    Nguyen Xuan Hoai & R. I. McKay

Authors
  1. Nguyen Xuan Hoai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. I. McKay
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hoai, N.X., McKay, R.I. (2004). Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_69

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24855-2_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation