Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure

  • Grant Dick
  • Peter A. Whigham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7831)


The concept of bloat — the increase of program size without a corresponding increase in fitness — presents a significant drawback to the application of genetic programming. One approach to controlling bloat, dubbed spatial structure with elitism (SS+E), uses a combination of spatial population structure and local elitist replacement to implicitly constrain unwarranted program growth. However, the default implementation of SS+E uses a replacement scheme that prevents the introduction of smaller programs in the presence of equal fitness. This paper introduces a modified SS+E approach in which replacement is done under a lexicographic parsimony scheme. The proposed model, spatial structure with lexicographic parsimonious elitism (SS+LPE), exhibits an improvement in bloat reduction and, in some cases, more effectively searches for fitter solutions.


Genetic Programming Symbolic Regression Program Size Redundant Code Standard Genetic Programming 
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.
    Bleuler, S., Brack, M., Thiele, L., Zitzler, E.: Multiobjective genetic programming: Reducing bloat using SPEA2. In: Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001, pp. 536–543. IEEE Press (2001)Google Scholar
  2. 2.
    Jackson, D.: The identification and exploitation of dormancy in genetic programming. Genetic Programming and Evolvable Machines 11, 89–121 (2010)CrossRefGoogle Scholar
  3. 3.
    Jong, K.A.D., Sarma, J.: On decentralizing selection algorithms. In: Eshelman, L.J. (ed.) Proceedings of the 6th International Conference on Genetic Algorithms, pp. 17–23. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
  4. 4.
    Kennedy, C.J., Giraud-Carrier, C.: A depth controlling strategy for strongly typed evolutionary programming. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, July 13-17, vol. 1, pp. 879–885. Morgan Kaufmann, Orlando (1999)Google Scholar
  5. 5.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  6. 6.
    Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Langdon, W.B., et al. (eds.) Proceedings of the Fourth International Conference on Genetic and Evolutionary Computation, GECCO 2002, pp. 829–836. Morgan Kaufmann (2002)Google Scholar
  7. 7.
    Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3), 309–344 (2006)CrossRefGoogle Scholar
  8. 8.
    McDermott, J., White, D.R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., Jaskowski, W., Krawiec, K., Harper, R., De Jong, K., O’Reilly, U.M.: Genetic programming needs better benchmarks. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 791–798. ACM, New York (2012)CrossRefGoogle Scholar
  9. 9.
    Pagie, L., Hogeweg, P.: Evolutionary consequences of coevolving targets. Evolutionary Computation 5(4), 401–418 (1997)CrossRefGoogle Scholar
  10. 10.
    Poli, R.: A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 204–217. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guild to Genetic Programming, pp. 102–103. (2008)Google Scholar
  12. 12.
    Silva, S., Almeida, J.: Dynamic Maximum Tree Depth: A Simple Technique for Avoiding Bloat in Tree-Based GP. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, D., Roy, R., O’Reilly, U.M., Beyer, H.G., Standish, R., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A.C., Dowsland, K., Jonoska, N., Miller, J. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1776–1787. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Soule, T., Foster, J., Dickinson, J.: Code growth in genetic programming. In: Koza, J., Goldberg, D., Fogel, D., Riolo, R. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 215–223. MIT Press, Stanford University (1996)Google Scholar
  14. 14.
    Soule, T., Heckendorn, R.: An analysis of the causes of code growth in genetic programming. Genetic Programming and Evolvable Machines 3(3), 283–309 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Teller, A.: The evolution of mental models. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, ch. 9, pp. 199–217. MIT Press (1994)Google Scholar
  16. 16.
    Whigham, P.A., Dick, G.: Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation 14(2), 173–190 (2010)CrossRefGoogle Scholar
  17. 17.
    Wong, P., Zhang, M.: Algebraic simplification of GP programs during evolution. In: Proceedings of the Eighth International Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 927–934. ACM Press, Seattle (2006)Google Scholar
  18. 18.
    Yamaguchi, H., Hiroyasu, T., Nunokawa, S., Koizumi, N., Okumura, N., Yokouchi, H., Miki, M., Yoshimi, M.: Comparison study of controlling bloat model of GP in constructing filter for cell image segmentation problems. In: 2012 IEEE Congress on Evolutionary Computation, CEC, pp. 3468–3475 (2012)Google Scholar
  19. 19.
    Zhang, B.T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Grant Dick
    • 1
  • Peter A. Whigham
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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