Examining Mutation Landscapes in Grammar Based Genetic Programming

  • Eoin Murphy
  • Michael O’Neill
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)


Representation is a very important component of any evolutionary algorithm. Changing the representation can cause an algorithm to perform very differently. Such a change can have an effect that is difficult to understand. This paper examines what happens to the grammatical evolution algorithm when replacing the commonly used context-free grammar representation with a tree-adjunct grammar representation. We model the landscapes produced when using integer flip mutation with both representations and compare these landscapes using visualisation methods little used in the field of genetic programming.


Genetic Programming Landscape Model Derivation Tree Symbolic Regression Grammatical Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Abbass, H., Hoai, N.X., McKay, R.I.: AntTAG: A new method to compose computer programs using colonies of ants. In: Proceedings of 2002 World Congress on Computational Intelligence, vol. 2, pp. 1654–1666. IEEE Press, Los Alamitos (2002)Google Scholar
  2. 2.
    Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. SCI. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Gathercole, C., Ross, P.: An adverse interaction between crossover and restricted tree depth in genetic programming. In: GECCO 1996: Proceedings of the First Annual Conference on Genetic Programming, pp. 291–296. MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Harper, R.: GE, explosive grammars and the lasting legacy of bad initialisation. In: IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, July 18-23, IEEE Press, Los Alamitos (2010)Google Scholar
  5. 5.
    Hoai, N.X.: Solving the symbolic regression with tree-adjunct grammar guided genetic programming: The preliminary results. In: Australasia-Japan Workshop on Intelligent and Evolutionary Systems, University of Otago, Dunedin, New Zealand, November 19-21 (2001)Google Scholar
  6. 6.
    Hoai, N.X., McKay, R.I., Essam, D., Chau, R.: Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: The comparative results. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, May 12-17, pp. 1326–1331. IEEE Press, Los Alamitos (2002)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Hoai, N.X. (Bob) McKay, R.I., Essam, D.: Representation and structural difficulty in genetic programming. IEEE Transactions on Evolutionary Computation 10(2), 157–166 (2006)CrossRefGoogle Scholar
  9. 9.
    Jones, T.: Evolutionary Algorithms, Fitness Landscapes, and Search. PhD thesis, University of New Mexico (1995)Google Scholar
  10. 10.
    Joshi, A.K., Schabes, Y.: Tree-Adjoining Grammars. Handbook of Formal Languages, Beyond Words 3, 69–123 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Joshi, A.K., Levy, L.S., Takahashi, M.: Tree adjunct grammars. Journal of Computer and System Sciences 10(1), 136–163 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Koza, J.R., Poli, R.: Genetic programming. In: Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, ch. 5, pp. 127–164. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  14. 14.
    Kroch, A., Joshi, A.K.: The Linguistic Relevance of Tree Adjoining Grammar, Technical Report, University of Pennsylvania (1985)Google Scholar
  15. 15.
    McKay, R., Hoai, N., Whigham, P., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genetic Programming and Evolvable Machines 11, 365–396 (2010)CrossRefGoogle Scholar
  16. 16.
    Murphy, E., O’Neill, M., Galvan-Lopez, E., Brabazon, A.: Tree-adjunct grammatical evolution. In: 2010 IEEE World Congress on Computational Intelligence, IEEE Computational Intelligence Society, Barcelona, Spain, July 18-23, pp. 4449–4456. IEEE Press, Los Alamitos (2010)Google Scholar
  17. 17.
    Nguyen, X.H. (Bob) McKay, R.I.: A framework for tree-adjunct grammar guided genetic programming. In: Post-graduate ADFA Conference on Computer Science, Canberra, Australia, pp. 93–100 (2001)Google Scholar
  18. 18.
    Nilsson, N.J.: Problem-Solving Methods in Artificial Intelligence. McGraw-Hill Pub. Co., New York (1971)Google Scholar
  19. 19.
    O’Neill, M., Brabazon, A., Nicolau, M., Garraghy, S.M., Keenan, P.: πGrammatical Evolution. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 617–629. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    O’Neill, M., Fagan, D., Galvan, E., Brabazon, A., McGarraghy, S.: An analysis of Genotype-Phenotype Maps in Grammatical Evolution. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, Springer, Heidelberg (2010)Google Scholar
  21. 21.
    O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. Genetic programming, vol. 4. Kluwer Academic Publishers, Dordrecht (2003)CrossRefzbMATHGoogle Scholar
  22. 22.
    Pearl, J.: Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley Longman Publishing Co., Inc., Boston (1984)Google Scholar
  23. 23.
    Rich, E.: Artificial intelligence. McGraw-Hill, Inc., New York (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eoin Murphy
    • 1
  • Michael O’Neill
    • 1
  • Anthony Brabazon
    • 1
  1. 1.Natural Computing Research and Applications GroupUniveristy College DublinIreland

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