Skip to main content

Advertisement

SpringerLink
Log in

Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

An Erratum to this article was published on 01 March 2006

Abstract

Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. H. Abramson and V. Dahl, Logic Grammars, Springer-Verlag: Berlin, 1989.

    Google Scholar 

  2. P. J. Angeline, “Two self-adaptive crossover operators for genetic programming,” in Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear Jr. (Eds.), MIT Press: MA, 1996, pp. 89–109.

    Google Scholar 

  3. P. J. Angeline and K. E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press: MA, 1996.

    Google Scholar 

  4. S. Brave, “Evolving recursive programs for tree search,” in Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear Jr. (Eds.), MIT Press: MA, 1996, pp. 203–219.

    Google Scholar 

  5. A. A. Freitas, “A genetic programming framework for two data mining tasks: Classification and generalized rule induction,” in Genetic Programming 1997: Proceedings of the 2nd Annual Conference, 1997, pp. 96–101.

  6. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley: Reading, MA, 1989.

    Google Scholar 

  7. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor, 1975.

    Google Scholar 

  8. J. E. Hopcroft and J. D. Ullman, Introduction to Automata Theory, Languages, and Computation, Addison-Wesley: MA, 1979.

    Google Scholar 

  9. M. Keijzer and V. Babovic, “Declarative and preferential bias in GP-based scientific discovery,” Genetic Programming and Evolvable Machines vol. 3, pp. 41–79, 2002.

    Article  Google Scholar 

  10. M. Keijzer, V. Babovic, C. Ryan, M. O'Neill, and M. Cattolico, “Adaptive logic programming,” in Proceedings of the Genetic and Evolutionary Computation Conference 2001, L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke (Eds.), Morgan Kaufmann: CA, 2001, pp. 42–49.

    Google Scholar 

  11. K. E. Kinnear Jr. (Ed.), Advances in Genetic Programming 1, MIT Press: MA, 1994.

    Google Scholar 

  12. J. R. Koza, F. H. Bennett III, D. Andre, and M. A. Keane, Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann Publishers: San Francisco, CA, 1999.

    Google Scholar 

  13. J. R. Koza, M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu, and G. Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer Academic Publishers: Boston, MI, 2003.

    Google Scholar 

  14. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press: Cambridge, MA, 1992.

    Google Scholar 

  15. J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press: Cambridge MA, 1994.

    Google Scholar 

  16. N. Lavrac and S. Dzeroski, Inductive Logic Programming: Techniques and Applications, Ellis Horword: London, 1994.

    Google Scholar 

  17. D. J. Montana, “Strongly typed genetic programming,” Evolutionary Computation vol. 3, pp. 199–230, 1995.

    Google Scholar 

  18. S. Muggletion, “Inductive logic programming,” in Inductive Logic Programming, S. Muggletion (Ed.), Academic Press: London, 1992, pp. 3–27.

    Google Scholar 

  19. M. O'Neill and C. Ryan, “Grammatical evolution,” IEEE Transactions on Evolutionary Computation vol. 5, pp. 349–58, 2001.

    Article  Google Scholar 

  20. M. O'Neill and C. Ryan, “Grammatical evolution by grammatical evolution: The evolution of grammar and genetic code,” in Proceedings of the Seventh European Conference on Genetic Programming, 2004, pp. 138–149.

  21. F. C. N. Pereira and S. M. Shieber, Prolog and Natural-Language Analysis, CSLI: CA, 1987.

    Google Scholar 

  22. F. C. N. Pereira and D. H. D. Warren, “Definite clause grammars for language analysis—A survey of the formalism and a comparison with augmented transition networks,” Artificial Intelligence vol. 13, pp. 231–278, 1980.

    Article  MathSciNet  Google Scholar 

  23. Y. Shan, R. I. McKay, R. Baxter, H. Abbass, D. Essam, and H. X. Nguyen, “Grammar model-based program evolution,” in Proceedings of the 2004 IEEE Congress on Evolutionary Computation, 2004, pp. 478–485.

  24. L. Spector, W. B. Langdon, U. M. O'Reilly, and P. J. Angeline, (Eds.), Advances in Genetic Programming 3. MIT Press: MA, 1999.

    Google Scholar 

  25. L. R. Tang, M. E. Califf, and R. J. Mooney, “An experimental comparison of genetic programming and inductive logic programming on learning recursive list functions,” TR AI98-271, Artificial Intelligence Lab, University of Texas at Austin, 1998.

  26. P. A. Whigham, Grammatical Bias for Evolutionary Learning, PhD Thesis. University of New South Wales, 1996.

  27. P. A. Whigham, “Search bias, language bias, and genetic programming,” in Genetic Programming 1996: Proceedings of the First Annual Conference, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo (Eds.), MIT Press: MA, 1996, pp. 230–237.

    Google Scholar 

  28. P. A. Whigham and R. McKay, “Genetic approaches to learning recursive relations,” Lecture Notes in Artificial Intelligence, vol. 956, pp. 17–28, 1995.

    Google Scholar 

  29. M. L. Wong, “A flexible knowledge discovery system using genetic programming and logic grammars,” Decision Support Systems vol. 31, pp. 405–428, 2001.

  30. M. L. Wong and K. S. Leung, “Evolving recursive functions for the even-parity problem using genetic programming,” in Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear Jr. (Eds.), MIT Press: MA, 1996, pp. 221–240.

    Google Scholar 

  31. M. L. Wong and K. S. Leung, “Learning recursive functions from noisy examples using generic genetic programming,” in Genetic Programming 1996: Proceedings of the First Annual Conference, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo (Eds.), MIT Press: MA, 1996, pp. 238–246.

    Google Scholar 

  32. M. L. Wong and K. S. Leung, “Evolutionary program induction directed by logic grammars,” Evolutionary Computation vol. 5, pp. 143–180, 1997.

    Google Scholar 

  33. M. L. Wong and K. S. Leung, Data mining using grammar based genetic programming and the applications, Kluwer Academic Publishers: Boston, MA, 2000.

    Google Scholar 

  34. T. Yu, An Analysis of the Impact of Functional Programming Techniques on Genetic Programming, PhD Thesis. Department of Computer Science, University College London, 1999.

  35. T. Yu, “Hierarchical processing for evolving recursive and modular programs using higher-order functions and lambda abstraction,” Genetic Programming and Evolvable Machines vol. 2, pp. 345–380, 2001.

    Article  MATH  Google Scholar 

  36. T. Yu, “Polymorphism and genetic programming,” in Proceedings of the Fourth European Conference on Genetic Programming, 2001, pp. 416–421.

  37. T. Yu and C. Clack, “PolyGP: A polymorphic genetic programming system in haskell,” in Genetic Programming 1998: Proceedings of the Third Annual Conference, J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. Riolo (Eds.), Morgan Kaufmann: CA, 1998, pp. 416–421.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing and Decision Sciences, Lingnan University, Hong Kong

    Man Leung Wong & Tuen Mun

Authors
  1. Man Leung Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tuen Mun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Man Leung Wong.

Additional information

Communicated by: William B. Langdon

An erratum to this article is available at http://dx.doi.org/10.1007/s10710-006-7455-6.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wong, M.L., Mun, T. Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming. Genet Program Evolvable Mach 6, 421–455 (2005). https://doi.org/10.1007/s10710-005-4805-8

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-005-4805-8

Keywords

Search

Navigation