NEAT, There’s No Bloat

  • Leonardo Trujillo
  • Luis Muñoz
  • Enrique Naredo
  • Yuliana Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)


The Operator Equalization (OE) family of bloat control methods have achieved promising results in many domains. In particular, the Flat-OE method, that promotes a flat distribution of program sizes, is one of the simplest OE methods and achieves some of the best results. However, Flat-OE, like all OE variants, can be computationally expensive. This work proposes a simplified strategy for bloat control based on Flat-OE. In particular, bloat is studied in the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. NEAT includes a very simple diversity preservation technique based on speciation and fitness sharing, and it is hypothesized that with some minor tuning, speciation in NEAT can promote a flat distribution of program size. Results indicate that this is the case in two benchmark problems, in accordance with results for Flat-OE. In conclusion, NEAT provides a worthwhile strategy that could be extrapolated to other GP systems, for effective and simple bloat control.


NEAT Bloat Operator Equalization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11(3-4), 339–363 (2010)CrossRefGoogle Scholar
  2. 2.
    Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Naredo, E., Trujillo, L., Martínez, Y.: Searching for novel classifiers. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 145–156. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Naredo, E., Trujillo, L.: Searching for novel clustering programs. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2013, pp. 1093–1100. ACM, New York (2013)CrossRefGoogle Scholar
  5. 5.
    Trujillo, L., Naredo, E., Martínez, Y.: Preliminary study of bloat in genetic programming with behavior-based search. In: Emmerich, M., et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. AISC, vol. 227, pp. 293–305. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Silva, S., Costa, E.: Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141–179 (2009)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Dignum, S., Poli, R.: Operator equalisation and bloat free gp. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 110–121. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Silva, S., Dignum, S., Vanneschi, L.: Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genetic Programming and Evolvable Machines 13(2), 197–238 (2012)CrossRefGoogle Scholar
  9. 9.
    Silva, S.: Reassembling operator equalisation: a secret revealed. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1395–1402. ACM, New York (2011)Google Scholar
  10. 10.
    Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)CrossRefGoogle Scholar
  11. 11.
    Langdon, W.B., Poli, R.: Fitness causes bloat. In: Proceedings of the Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer (1997)Google Scholar
  12. 12.
    Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)CrossRefGoogle Scholar
  13. 13.
    Poli, R., Langdon, W.B., Dignum, S.: On the limiting distribution of program sizes in tree-based genetic programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 193–204. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Altenberg, L.: The evolution of evolvability in genetic programming. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 47–74. MIT Press, Cambridge (1994)Google Scholar
  15. 15.
    Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 41–49. Erlbaum Associates Inc., Hillsdale (1987)Google Scholar
  16. 16.
    Trujillo, L., Olague, G., Lutton, E., Fernández de Vega, F., Dozal, L., Clemente, E.: Speciation in behavioral space for evolutionary robotics. Journal of Intelligent & Robotic Systems 64(3-4), 323–351 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Leonardo Trujillo
    • 1
  • Luis Muñoz
    • 1
  • Enrique Naredo
    • 1
  • Yuliana Martínez
    • 1
  1. 1.Tree-Lab, Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería, Eléctrica y ElectrónicaInstituto Tecnológico de TijuanaTijuana B.C.México

Personalised recommendations