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Structured Grammatical Evolution: A Dynamic Approach

  • Nuno LourençoEmail author
  • Filipe Assunção
  • Francisco B. Pereira
  • Ernesto Costa
  • Penousal Machado
Chapter

Abstract

Grammars have attracted the attention of researchers within the Evolutionary Computation field, specially from the Genetic Programming community. The most successful example of the use of grammars by GP is Grammatical Evolution (GE). In spite of being widely used by practitioners of different fields, GE is not free from drawbacks. The ones that are most commonly pointed out are those linked with redundancy and locality of the representation. To address these limitations Structured Grammatical Evolution (SGE) was proposed, which introduces a one-to-one mapping between the genotype and the non-terminals. In SGE the input grammar must be pre-processed so that recursion is removed, and the maximum number of expansion possibilities for each symbol determined. This has been pointed out as a drawback of SGE and to tackle it we introduce Dynamic Structured Grammatical Evolution (DSGE). In DSGE there is no need to pre-process the grammar, as it is expanded on the fly during the evolutionary process, and thus we only need to define the maximum tree depth. Additionally, it only encodes the integers that are used in the genotype to phenotype mapping, and grows as needed during evolution. Experiments comparing DSGE with SGE show that DSGE performance is never worse than SGE, being statistically superior in a considerable number of the tested problems.

Notes

Acknowledgements

This research is partially funded by: Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grant SFRH/BD/114865/2016. We gratefully acknowledge the support of NVIDIA Corporation for the donation of a Titan X GPU. We would also like to thank Tiago Martins for all the patience making the charts herein presented.

References

  1. 1.
    F. Assunção, N. Lourenço, P. Machado, B. Ribeiro, Towards the evolution of multi-layered neural networks: a dynamic structured grammatical evolution approach, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17 (ACM, New York, 2017), pp. 393–400. https://doi.org/10.1145/3071178.3071286 Google Scholar
  2. 2.
    F. Assunção, N. Lourenço, P. Machado, B. Ribeiro, Automatic generation of neural networks with structured grammatical evolution, in 2017 IEEE Congress on Evolutionary Computation (CEC) (2017), pp. 1557–1564.  https://doi.org/10.1109/CEC.2017.7969488
  3. 3.
    J. Byrne, M. O’Neill, A. Brabazon, Structural and nodal mutation in grammatical evolution, in Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, New York, 2009, pp. 1881–1882Google Scholar
  4. 4.
    J. Byrne, M. O’Neill, J. McDermott, A. Brabazon, An analysis of the behaviour of mutation in grammatical evolution, in Genetic Programming. Lecture Notes in Computer Science, vol. 6021 (Springer, Berlin, 2010), pp. 14–25Google Scholar
  5. 5.
    A. Field, Discovering Statistics Using IBM SPSS Statistics (Sage, Los Angeles, 2013)Google Scholar
  6. 6.
    R. Franz, Representations for Genetic and Evolutionary Algorithms (Springer, Berlin, 2006)Google Scholar
  7. 7.
    L. Fu, E. Medico, FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinf. 8(1), 1 (2007)Google Scholar
  8. 8.
    R.P. Gorman, T.J. Sejnowski, Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw. 1(1), 75–89 (1988)CrossRefGoogle Scholar
  9. 9.
    R. Harper, Spatial co-evolution: quicker, fitter and less bloated, in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2012), pp. 759–766Google Scholar
  10. 10.
    J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1 (MIT Press, Cambridge, 1992)zbMATHGoogle Scholar
  11. 11.
    M. Lichman, UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
  12. 12.
    N. Lourenço, F.B. Pereira, E. Costa, Unveiling the properties of structured grammatical evolution. Genet. Program. Evolvable Mach. 17(3), 251–289 (2016)CrossRefGoogle Scholar
  13. 13.
    J. McDermott, D.R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, U.M. O’Reilly, Genetic programming needs better benchmarks, in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2012), pp. 791–798Google Scholar
  14. 14.
    R.I. Mckay, N.X. Hoai, P.A. Whigham, Y. Shan, M. O’Neill, Grammar-based genetic programming: a survey. Genet. Program. Evolvable Mach. 11(3–4), 365–396 (2010)CrossRefGoogle Scholar
  15. 15.
    M. O’Neill, C. Ryan, Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)CrossRefGoogle Scholar
  16. 16.
    M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. Genetic Programming, vol. 4 (Kluwer Academic, Boston, 2003)Google Scholar
  17. 17.
    F. Rothlauf, On the locality of representations, in Genetic and Evolutionary Computation Conference. Lecture Notes in Computer Science (2003), pp. 1608–1609Google Scholar
  18. 18.
    F. Rothlauf, M. Oetzel, On the locality of grammatical evolution, in European Conference on Genetic Programming (Springer, Berlin, 2006), pp. 320–330Google Scholar
  19. 19.
    C. Ryan, J. Collins, M.O. Neill, Grammatical evolution: evolving programs for an arbitrary language, in European Conference on Genetic Programming (Springer, Berlin, 1998), pp. 83–96Google Scholar
  20. 20.
    V.G. Sigillito, S.P. Wing, L.V. Hutton, K.B. Baker, Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech. Dig. 10(3), 262–266 (1989)Google Scholar
  21. 21.
    W.N. Street, W.H. Wolberg, O.L. Mangasarian, Nuclear feature extraction for breast tumor diagnosis, in IS&T/SPIE’s Symposium on Electronic Imaging: Science and Technology (International Society for Optics and Photonics, Bellingham, 1993), pp. 861–870Google Scholar
  22. 22.
    P.A. Whigham, et al.: Grammatically-based genetic programming, in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, vol. 16 (1995), pp. 33–41Google Scholar
  23. 23.
    P.A. Whigham, G. Dick, J. Maclaurin, C.A. Owen, Examining the best of both worlds of grammatical evolution, in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2015), pp. 1111–1118Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nuno Lourenço
    • 1
    Email author
  • Filipe Assunção
    • 1
  • Francisco B. Pereira
    • 2
    • 3
  • Ernesto Costa
    • 1
  • Penousal Machado
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.Polytechnic Institute of CoimbraQuinta da NoraCoimbraPortugal

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