Population Clustering in Genetic Programming

  • Huayang Xie
  • Mengjie Zhang
  • Peter Andreae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)


This paper proposes an approach to reducing the cost of fitness evaluation whilst improving the effectiveness in Genetic Programming (GP). In our approach, the whole population is first clustered by a heuristic called fitness-case-equivalence. Then a cluster representative is selected for each cluster. The fitness value of the representative is calculated on all training cases. The fitness is then directly assigned to other members in the same cluster. Subsequently, a clustering tournament selection method replaces the standard tournament selection method. A series of experiments were conducted to solve a symbolic regression problem, a binary classification problem, and a multi-class classification problem. The experiment results show that the new GP system significantly outperforms the standard GP system on these problems.


Genetic Programming Fitness Evaluation Tournament Selection Population Cluster Training Case 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Giacobini, M., Tomassini, M., Vanneschi, L.: Limiting the number of fitness cases in genetic programming using statistics. In: PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, pp. 371–380. Springer, London (2002)Google Scholar
  2. 2.
    Ziegler, J., Banzhaf, W.: Decreasing the number of evaluations in evolutionary algorithms by using a meta-model of the fitness function. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 264–275. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Sastry, K., Goldberg, D.E., Pelikan, M.: Don’t evaluate, inherit. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 551–558. Morgan Kaufmann, San Francisco (2001)Google Scholar
  4. 4.
    Kim, H.S., Cho, S.B.: An efficient genetic algorithms with less fitness evaluation by clustering. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 887–894. IEEE, Los Alamitos (2001)Google Scholar
  5. 5.
    Jin, Y., Sendhoff, B.: Reducing fitness evaluations using clustering techniques and neural network ensembles. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 688–699. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Altenberg, L.: Emergent phenomena in genetic programming. In: Sebald, A.V., Fogel, L.J. (eds.) Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 233–241. World Scientific, Singapore (1994)Google Scholar
  7. 7.
    Tackett, W.A.: Recombination, selection, and the genetic construction of computer programs. PhD thesis, University of Southern California, Los Angeles, CA, USA (1994)Google Scholar
  8. 8.
    Jackson, D.: Fitness evaluation avoidance in boolean GP problems. In: Corne, D., et al. (eds.) Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2530–2536. IEEE Press, Edinburgh (2005)CrossRefGoogle Scholar
  9. 9.
    Xie, H.: Diversity control in GP with ADF for regression tasks. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1253–1257. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Frey, P.W., Slate, D.J.: Letter recognition using Holland-style adaptive classifiers. Machine Learning 6, 161–182 (1991)Google Scholar
  11. 11.
    Jin, Y., Hüsken, M., Olhofer, M., Sendhoff, B.: Neural networks for fitness approximation in evolutionary optimization. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 281–305. Springer, Berlin (2004)Google Scholar
  12. 12.
    Salami, M., Hendtlass, T.: The fast evaluation strategy for evolvable hardware. Genetic Programming and Evolvable Machines 6, 139–162 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huayang Xie
    • 1
  • Mengjie Zhang
    • 1
  • Peter Andreae
    • 1
  1. 1.School of Mathematics, Statistics and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations