A Comparative Study of Different Grammar-Based Genetic Programming Approaches

  • Nuno Lourenço
  • Joaquim Ferrer
  • Francisco B. Pereira
  • Ernesto Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10196)

Abstract

Grammars are useful formalisms to specify constraints, and not surprisingly, they have attracted the attention of Evolutionary Computation (EC) researchers to enforce problem restrictions. Context-Free-Grammar GP (CFG-GP) established the foundations for the application of grammars in Genetic Programming (GP), whilst Grammatical Evolution (GE) popularised the use of these approaches, becoming one of the most used GP variants. However, studies have shown that GE suffers from issues that have impact on its performance. To minimise these issues, several extensions have been proposed, which made the distinction between GE and CFG-GP less noticeable. Another direction was followed by Structured Grammatical Evolution (SGE) that maintains the separation between genotype and phenotype from GE, but overcomes most of its issues. Our goal is to perform a comparative study between CFG-GP, GE and SGE to examine their relative performance. The results show that in most of the selected benchmarks, CFG-GP and SGE have a similar performance, showing that SGE is a good alternative to GE.

Keywords

Genetic programming Grammar-based genetic programming Grammatical evolution 

References

  1. 1.
    Byrne, J., O’Neill, M., Brabazon, A.: Structural and nodal mutation in grammatical evolution. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, New York, pp. 1881–1882 (2009)Google Scholar
  2. 2.
    Byrne, J., O’Neill, M., McDermott, J., Brabazon, A.: An analysis of the behaviour of mutation in grammatical evolution. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 14–25. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12148-7_2 CrossRefGoogle Scholar
  3. 3.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)MATHGoogle Scholar
  4. 4.
    Langdon, W.B., Poli, R.: Why ants are hard. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, 22–25 July 1998, pp. 193–201. Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA (1998)Google Scholar
  5. 5.
    Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
  6. 6.
    Lourenço, N., Pereira, F.B., Costa, E.: Unveiling the properties of structured grammatical evolution. Genet. Program. Evolvable Mach. 17(3), 251–289 (2016)CrossRefGoogle Scholar
  7. 7.
    Mckay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., ONeill, M.: Grammar-based genetic programming: a survey. Genet. Program. Evolvable Mach. 11(3–4), 365–396 (2010)CrossRefGoogle Scholar
  8. 8.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)CrossRefGoogle Scholar
  9. 9.
    ONeill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Genetic Programming, vol. 4. Springer, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Rothlauf, F.: On the locality of representations. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1608–1609. Springer, Heidelberg (2003). doi:10.1007/3-540-45110-2_48 CrossRefGoogle Scholar
  11. 11.
    Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms. Springer, Heidelberg (2006)CrossRefMATHGoogle Scholar
  12. 12.
    Rothlauf, F., Oetzel, M.: On the locality of grammatical evolution. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 320–330. Springer, Heidelberg (2006). doi:10.1007/11729976_29 CrossRefGoogle Scholar
  13. 13.
    Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998). doi:10.1007/BFb0055930 CrossRefGoogle Scholar
  14. 14.
    Whigham, P.A.: Inductive bias and genetic programming. In: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA (Conf. Publ. No. 414), pp. 461–466. IET (1995)Google Scholar
  15. 15.
    Whigham, P.A., Dick, G., Maclaurin, J., Owen, C.A.: Examining the best of both worlds of grammatical evolution. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1111–1118. ACM (2015)Google Scholar
  16. 16.
    Whigham, P.A., et al.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, vol. 16, pp. 33–41 (1995)Google Scholar
  17. 17.
    White, B.C., Reif, D.M., Gilbert, J.C., Moore, J.H.: A statistical comparison of grammatical evolution strategies in the domain of human genetics. In: 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 491–497. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nuno Lourenço
    • 1
  • Joaquim Ferrer
    • 1
  • Francisco B. Pereira
    • 1
    • 2
  • Ernesto Costa
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.Instituto Superior de Engenharia de CoimbraCoimbraPortugal

Personalised recommendations