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Altering Search Rates of the Meta and Solution Grammars in the mGGA

  • Erik Hemberg
  • Michael O’Neill
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4971)

Abstract

Adopting a meta-Grammar with Grammatical Evolution(GE) allows GE to evolve the grammar that it uses to specify the construction of a syntactically correct solution. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved during a run. This can lead to improved performance over the standard static grammar in terms of adapting to a dynamic environment and improved scalability to larger problem instances. This approach allows the evolution of modularity and reuse both on structural and symbol levels resulting in a compression of the representation of a solution. In this paper an analysis of altering the rate of sampling of the evolved solution grammars is undertaken. It is found that the majority of evolutionary search is currently focused on the generation of the solution grammars to such an extent that the candidate solutions are often hard-coded into them making the solution chromosome effectively redundant. This opens the door to future work in which we can explore how the search can be better balanced between the meta and solution grammars

Keywords

Mutation Rate Problem Instance Fitness Evaluation Evolutionary Search 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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References

  1. 1.
    O’Neill, M., Ryan, C.: Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code. In: Keijzer, M., O’Reilly, U.-M., Lucas, S.M., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 138–149. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer, Dordrecht (2003)zbMATHGoogle Scholar
  3. 3.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for financial Modelling. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 4.
    Cleary, R., O’Neill, M.: An attribute grammar decoder for the 01 multiconstrained knapsack problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 34–45. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Shan, Y., McKay, R.I., Baxter, R., Abbass, H., Essam, D., Hoai, N.X.: Grammar model-based program evolution. In: Proc. of CEC 2004, pp. 478–485. IEEE Press, Los Alamitos (2004)Google Scholar
  6. 6.
    O’Neill, M., Brabazon, A.: mGGA: The meta-grammar genetic algorithm. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J.I., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 311–320. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Dempsey, I., O’Neill, M., Brabazon, A.: Meta-grammar constant creation with grammatical evolution by grammatical evolution. In: Beyer, H.-G., O’Reilly, U.-M. (eds.) Proc. of GECCO 2005, vol. 2, pp. 1665–1671. ACM Press, New York (2005)CrossRefGoogle Scholar
  8. 8.
    Hemberg, E., Gilligan, C., O’Neill, M., Brabazon, A.: A grammatical genetic programming approach to modularity in genetic algorithms. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 1–11. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Dempsey, I.: Grammatical Evolution in Dynamic Environments. PhD thesis, University College Dublin (2007)Google Scholar
  10. 10.
    Garibay, O.O., Garibay, I.I., Wu, A.S.: The modular genetic algorithm: Exploiting regularities in the problem space. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 584–591. Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B 57(1), 289–300 (1995)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Erik Hemberg
    • 1
  • Michael O’Neill
    • 1
  • Anthony Brabazon
    • 1
  1. 1.Natural Computing Research & Applications GroupUniversity College DublinIreland

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