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Comparing Methods to Creating Constants in Grammatical Evolution

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Abstract

This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter calls compact methods to constant creation. Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. Against common wisdom, a standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. In fact, while GP characteristically evolves increasingly larger individuals, GE—after an initial growth or drop in sizes—tends to keep individual sizes stable despite no explicit mechanisms to control size growth. Furthermore, various GE setups perform acceptably well on unseen test data and typically outperform GP. Overall, these results encourage a belief that standard GE methods to symbolic regression are relatively resistant to pathogenic evolutionary tendencies of code bloat and overfitting.

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Notes

  1. 1.

    http://cswww.essex.ac.uk/staff/rpoli/TinyGP/.

References

  1. D.A. Augusto, H.J.C. Barbosa, A. da Motta Salles Barreto, H.S. Bernardino, Evolving numerical constants in grammatical evolution with the ephemeral constant method, in Proceedings 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, Lisbon, 10–13 Oct 2011, ed. by L. Antunes, H.S. Pinto. Lecture Notes in Computer Science, vol. 7026 (2011), pp. 110–124

    Google Scholar 

  2. D.A. Augusto, H.J.C. Barbosa, A.M.S. Barreto, H.S. Bernardino, A new approach for generating numerical constants in grammatical evolution, in GECCO ’11: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, Dublin, 12–16 July 2011, ed. by N. Krasnogor, P.L. Lanzi, A. Engelbrecht, D. Pelta, C. Gershenson, G. Squillero, A. Freitas, M. Ritchie, M. Preuss, C. Gagne, Y.S. Ong, G. Raidl, M. Gallager, J. Lozano, C. Coello-Coello, D.L. Silva, N. Hansen, S. Meyer-Nieberg, J. Smith, G. Eiben, E. Bernado-Mansilla, W. Browne, L. Spector, T. Yu, J. Clune, G. Hornby, M.L. Wong, P. Collet, S. Gustafson, J.P. Watson, M. Sipper, S. Poulding, G. Ochoa, M. Schoenauer, C. Witt, A. Auger (ACM, New York, 2011), pp. 193–194

    Google Scholar 

  3. W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and Its Applications (Morgan Kaufmann, San Francisco, 1998)

    MATH  Google Scholar 

  4. J. Byrne, M. O’Neill, E. Hemberg, A. Brabazon, Analysis of constant creation techniques on the binomial-3 problem with grammatical evolution, in 2009 IEEE Congress on Evolutionary Computation, Trondheim, 18–21 May 2009, ed. by A. Tyrrell (IEEE Computational Intelligence Society, IEEE Press, Piscataway, 2009), pp. 568–573

    Google Scholar 

  5. D. Costelloe, C. Ryan, On improving generalisation in genetic programming, in Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, Tuebingen, 15–17 Apr 2009, ed. by L. Vanneschi, S. Gustafson, A. Moraglio, I. De Falco, M. Ebner. LNCS, vol .5481 (Springer, Berlin, 2009), pp. 61–72

    Google Scholar 

  6. J.M. Daida, R.R. Bertram, S.A. Stanhope, J.C. Khoo, S.A. Chaudhary, O.A. Chaudhri, J.A. Polito II, What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming. Genet. Program. Evolvable Mach. 2(2), 165–191 (2001)

    Article  Google Scholar 

  7. I. Dempsey, M. O’Neill, A. Brabazon, Constant creation in grammatical evolution. Int. J. Innovative Comput. Appl. 1(1), 23–38 (2007)

    Article  Google Scholar 

  8. I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments. Studies in Computational Intelligence, vol. 194 (Springer, Berlin, 2009)

    Google Scholar 

  9. M. Evett, T. Fernandez, Numeric mutation improves the discovery of numeric constants in genetic programming, in Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, 22–25 July 1998, ed. by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R. Riolo (Morgan Kaufmann, San Francisco, 1998), pp. 66–71

    Google Scholar 

  10. M. Keijzer, Improving symbolic regression with interval arithmetic and linear scaling, in Genetic Programming, Proceedings of EuroGP’2003, Essex, 14–16 April 2003, ed. by C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, E. Costa. LNCS, vol. 2610 (Springer, Berlin, 2003), pp. 70–82

    Google Scholar 

  11. M. Keijzer, V. Babovic, Genetic programming, ensemble methods and the bias/variance tradeoff – introductory investigations, in Genetic Programming, Proceedings of EuroGP’2000, Edinburgh, 15–16 Apr 2000, ed. by R. Poli, W. Banzhaf, W.B. Langdon, J.F. Miller, P. Nordin, T.C. Fogarty. LNCS, vol. 1802 (Springer, Berlin, 2000), pp. 76–90

    Google Scholar 

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

    MATH  Google Scholar 

  13. S. Luke, L. Panait, A comparison of bloat control methods for genetic programming. Evol. Comput. 14(3), 309–344 (2006)

    Article  Google Scholar 

  14. B. McKay, M. Willis, D. Searson, G. Montague, Non-linear continuum regression using genetic programming, in Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, 13–17 July 1999, ed. by W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, R.E. Smith, vol. 2 (Morgan Kaufmann, San Francisco, 1999), pp. 1106–1111

    Google Scholar 

  15. T.M. Mitchell, Machine Learning, 1st edn. (McGraw-Hill, New York, 1997)

    MATH  Google Scholar 

  16. M. Nicolau, I. Dempsey, Introducing grammar based extensions for grammatical evolution, in Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, 6–21 July 2006, ed. by G.G. Yen, L. Wang, P. Bonissone, S.M. Lucas (IEEE Press, Piscataway, 2006), pp. 2663–2670

    Google Scholar 

  17. M. Nicolau, D. Slattery, libGE. Grammatical Evolution Library for version 0.27alpha1, 14 September 2006

    Google Scholar 

  18. M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. Genetic Programming, vol. 4 (Kluwer Academic Publishers, Boston, 2003)

    Google Scholar 

  19. M. O’Neill, C. Ryan, M. Keijzer, M. Cattolico, Crossover in grammatical evolution. Genet. Program. Evolvable Mach. 4(1), 67–93 (2003)

    Article  Google Scholar 

  20. R. Poli, A simple but theoretically-motivated method to control bloat in genetic programming, in Genetic Programming, Proceedings of EuroGP’2003, Essex, 14–16 Apr 2003, ed. by C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, E. Costa. LNCS, vol. 2610 (Springer, Berlin, 2003), pp. 204–217

    Google Scholar 

  21. C. Ryan, R.M.A. Azad, Sensible initialisation in grammatical evolution, in GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, Chicago, 11 July 2003, ed. by A.M. Barry (AAAI, New York, 2003), pp. 142–145

    Google Scholar 

  22. C. Ryan, M. Keijzer, An analysis of diversity of constants of genetic programming, in Genetic Programming, Proceedings of EuroGP’2003, Essex, 14–16 April 2003, ed. by C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, E. Costa. LNCS, vol. 2610 (Springer, Berlin, 2003), pp. 404–413

    Google Scholar 

  23. A. Topchy, W.F. Punch, Faster genetic programming based on local gradient search of numeric leaf values, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, 7–11 July 2001, ed. by L. Spector, E.D. Goodman, A. Wu, W.B. Langdon, H.M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M.H. Garzon, E. Burke (Morgan Kaufmann, San Francisco, 2001), pp. 155–162

    Google Scholar 

  24. E.J. Vladislavleva, G.F. Smits, D. den Hertog, Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Trans. Evol. Comput. 13(2), 333–349 (2009)

    Article  Google Scholar 

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Correspondence to R. Muhammad Atif Azad .

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Azad, R.M.A., Ryan, C. (2018). Comparing Methods to Creating Constants in Grammatical Evolution. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-78717-6_10

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