Evolving Cultural Learning Parameters in an NK Fitness Landscape

  • Dara Curran
  • Colm O’Riordan
  • Humphrey Sorensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4648)


Cultural learning allows individuals to acquire knowledge from others through non-genetic means. The effect of cultural learning on the evolution of artificial organisms has been the focus of much research. This paper examines the effects of cultural learning on the fitness and diversity of a population and, in addition, the effect of self-adaptive cultural learning parameters on the evolutionary process. The NK fitness landscape model is employed as the problem task and experiments employing populations endowed with both evolutionary and cultural learning are compared to those employing evolutionary learning alone.

Our experiments measure the fitness and diversity of both populations and also track the values of two self-adaptive cultural parameters. Results show that the addition of cultural learning has a beneficial effect on the population in terms of fitness and diversity maintenance. Furthermore, analysis of the self-adaptive parameter values shows the relative quality of the cultural process throughout the experiment and highlights the benefits of self-adaptation over fixed parameter values.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hinton, G.E., Nowlan, S.J.: How learning guides evolution. Complex Systems 1, 495–502 (1987)zbMATHGoogle Scholar
  2. 2.
    Nolfi, S., Parisi, D.: Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior 5(1), 75–97 (1996)CrossRefGoogle Scholar
  3. 3.
    Floreano, D., Mondada, F.: Evolution of plastic neurocontrollers for situated agents. In: Animals to Animats 4 (1996)Google Scholar
  4. 4.
    Sasaki, T., Tokoro, M.: Adaptation toward changing environments: Why darwinian in nature? In: Husbands, P., Harvey, I. (eds.) Fourth European Conference on Artificial Life, pp. 145–153. MIT Press, Cambridge (1997)Google Scholar
  5. 5.
    De Jong, E.D.: Analyzing the evolution of communication from a dynamical systems perspective. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 689–693. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    Best, M.L.: How culture can guide evolution: An inquiry into gene/meme enhancement and opposition. Adaptive Behavior 7(3/4), 289–306 (1999)CrossRefGoogle Scholar
  7. 7.
    Cangelosi, A.: Evolution of communication using combination of grounded symbols in populations of neural networks. In: Proceedings of IJCNN99 International Joint Conference on Neural Networks (vol. 6), Washington, DC, pp. 4365–4368. IEEE Computer Society Press, Los Alamitos (1999)CrossRefGoogle Scholar
  8. 8.
    Borenstein, E., Ruppin, E.: Enhancing autonomous agents evolution with learning by imitation. Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour 1(4), 335–348 (2003)Google Scholar
  9. 9.
    Curran, D., O’Riordan, C.: Applying cultural learning to sequential decision task problems. In: Proceedings of the 16th Irish Artificial Intelligence and Cognitive Science Conference (AICS 2005) (2005)Google Scholar
  10. 10.
    Darwin, C.: The Origin of Species: By Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Bantam Press, London (1859)Google Scholar
  11. 11.
    Cangelosi, A., Parisi, D.: The emergence of a language in an evolving population of neural networks. Technical Report NSAL–96004, National Research Council, Rome (1996)Google Scholar
  12. 12.
    Spector, L.: Genetic programming and AI planning systems. In: Proceedings of Twelfth National Conference on Artificial Intelligence, Seattle, Washington, USA, pp. 1329–1334. MIT Press, Cambridge (1994)Google Scholar
  13. 13.
    MacLennan, B., Burghardt, G.: Synthetic ethology and the evolution of cooperative communication. Adaptive Behavior 2(2), 161–188 (1993)CrossRefGoogle Scholar
  14. 14.
    Lamarck, J.B.: Philosophie Zoologique. Chez Dentu, Paris (1809)Google Scholar
  15. 15.
    Angeline, P.J.: Adaptive and self-adaptive evolutionary computations. In: Palaniswami, M., Attikiouzel, Y. (eds.) Computational Intelligence: A Dynamic Systems Perspective, pp. 152–163. IEEE Computer Society Press, Los Alamitos (1995), Google Scholar
  16. 16.
    Spears, W.M.: Adapting crossover in evolutionary algorithms. In: McDonnell, J.R., Reynolds, R.G., Fogel, D.B. (eds.) Proc. of the Fourth Annual Conference on Evolutionary Programming, pp. 367–384. MIT Press, Cambridge (1995)Google Scholar
  17. 17.
    Rosca, J.P.: Hierarchical self-organization in genetic programming. In: Proceedings of the Eleventh International Conference on Machine Learning (1994)Google Scholar
  18. 18.
    Bäck, T., Schwefel, H.P.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1 (1993)Google Scholar
  19. 19.
    Fogel, D.B., Fogel, L.J., Atmar, J.W.: Meta-evolutionary programming. In: Proceedings of the Conference on Signals, Systems, and Computers, pp. 540–545 (1991)Google Scholar
  20. 20.
    Brown, G.: Diversity in Neural Network Ensembles. PhD thesis, University of Birmingham (2003)Google Scholar
  21. 21.
    Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evolutionary Computation 8(1), 47–62 (2004)CrossRefGoogle Scholar
  22. 22.
    Curran, D., O’Riordan, C.: Increasing population diversity through cultural learning. Adaptive Behavior 14(4) (2006)Google Scholar
  23. 23.
    Kaufmann, S.A.: Adaptation on rugged fitness landscapes. Lectures in the Sciences of Complexity 1, 527–618 (1989)Google Scholar
  24. 24.
    Eriksson, R.I.: An initial analysis of the ability of learning to maintain diversity during incremental evolution. In: Freitas, A.A., Hart, W., Krasnogor, N., Smith, J. (eds.) Data Mining with Evolutionary Algorithms, Las Vegas, Nevada, USA, pp. 120–124 (2000),

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dara Curran
    • 1
  • Colm O’Riordan
    • 2
  • Humphrey Sorensen
    • 1
  1. 1.Dept. of Computer Science, University College Cork, Ireland 
  2. 2.Dept. of Information Technology, National University of Ireland, Galway 

Personalised recommendations