Searching for Novel Classifiers

  • Enrique Naredo
  • Leonardo Trujillo
  • Yuliana Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7831)


Natural evolution is an open-ended search process without an a priori fitness function that needs to be optimized. On the other hand, evolutionary algorithms (EAs) rely on a clear and quantitative objective. The Novelty Search algorithm (NS) substitutes fitness-based selection with a novelty criteria; i.e., individuals are chosen based on their uniqueness. To do so, individuals are described by the behaviors they exhibit, instead of their phenotype or genetic content. NS has mostly been used in evolutionary robotics, where the concept of behavioral space can be clearly defined. Instead, this work applies NS to a more general problem domain, classification. To this end, two behavioral descriptors are proposed, each describing a classifier’s performance from two different perspectives. Experimental results show that NS-based search can be used to derive effective classifiers. In particular, NS is best suited to solve difficult problems, where exploration needs to be encouraged and maintained.


Novelty Search Classification Genetic Programming 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dawkins, R.: Climbing Mount Improbable. W.W. Norton & Company (1996)Google Scholar
  2. 2.
    Kistemaker, S., Whiteson, S.: Critical factors in the performance of novelty search. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 965–972. ACM (2011)Google Scholar
  3. 3.
    Kowaliw, T., Dorin, A., McCormack, J.: Promoting creative design in interactive evolutionary computation. IEEE Transactions on Evolutionary Computation 16(4), 523–536 (2012)CrossRefGoogle Scholar
  4. 4.
    Koza, J.: Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines 11(3), 251–284 (2010)CrossRefGoogle Scholar
  5. 5.
    Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference on Artificial Life. ALIFE XI. MIT Press, Cambridge (2008)Google Scholar
  6. 6.
    Lehman, J., Stanley, K.O.: Efficiently evolving programs through the search for novelty. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 837–844. ACM (2010)Google Scholar
  7. 7.
    Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)CrossRefGoogle Scholar
  8. 8.
    Lehman, J., Stanley, K.O.: Evolving a diversity of virtual creatures through novelty search and local competition. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 211–218. ACM (2011)Google Scholar
  9. 9.
    Mouret, J.B., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: An empirical study. Evol. Comput. 20(1), 91–133 (2012)CrossRefGoogle Scholar
  10. 10.
    Ofria, C., Wilke, C.O.: Avida: a software platform for research in computational evolutionary biology. Artif. Life 10(2), 191–229 (2004)CrossRefGoogle Scholar
  11. 11.
    Silva, S., Almeida, J.: Gplab–a genetic programming toolbox for matlab. In: Gregersen, L. (ed.) Proceedings of the Nordic MATLAB Conference, pp. 273–278 (2003)Google Scholar
  12. 12.
    Trujillo, L., Martínez, Y., Galván-López, E., Legrand, P.: Predicting problem difficulty for genetic programming applied to data classification. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1355–1362. ACM, New York (2011)Google Scholar
  13. 13.
    Trujillo, L., Olague, G., Lutton, E., Fernández de Vega, F.: Discovering Several Robot Behaviors through Speciation. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 164–174. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Uy, N.Q., Hoai, N.X., O’Neill, M., Mckay, R.I., Galván-López, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genetic Programming and Evolvable Machines 12(2), 91–119 (2011)CrossRefGoogle Scholar
  16. 16.
    Woolley, B.G., Stanley, K.O.: Exploring promising stepping stones by combining novelty search with interactive evolution. CoRR abs/1207.6682 (2012)Google Scholar
  17. 17.
    Zhang, M., Smart, W.: Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recogn. Lett. 27(11), 1266–1274 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

Personalised recommendations