Measuring Diversity in Populations Employing Cultural Learning in Dynamic Environments

  • Dara Curran
  • Colm O’Riordan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)


This paper examines the effect of cultural learning on a population of neural networks. We compare the genotypic and phenotypic diversity of populations employing only population learning and of populations using both population and cultural learning in two types of dynamic environment: one where a single change occurs and one where changes are more frequent. We show that cultural learning is capable of achieving higher fitness levels and maintains a higher level of genotypic and phenotypic diversity.


Genetic Algorithm Phenotypic Diversity Genotypic Diversity Cultural Learning Neural Network Ensemble 
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.
    Brown, G.: Diversity in Neural Network Ensembles. PhD thesis, University of Birmingham (2003)Google Scholar
  2. 2.
    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
  3. 3.
    Chomsky, N.: On the nature of language. In: Origins and evolution of language and speech, vol. 280, pp. 46–57. Annals of the New York Academy of Science, New York (1976)Google Scholar
  4. 4.
    Denaro, D., Parisi, D.: Cultural evolution in a population of neural networks. In: Marinaro, M., Tagliaferri, R. (eds.) Neural Nets Wirn-96, pp. 100–111. Springer, New York (1996)Google Scholar
  5. 5.
    Kendall, G., Burke, E.K., Gustafson, S.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evolutionary Computation 8(1), 47–62 (2004)CrossRefGoogle Scholar
  6. 6.
    Grefenstette, J.J.: Genetic algorithms for dynamic environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2, pp. 137–144 (1992)Google Scholar
  7. 7.
    Hutchins, E., Hazlehurst, B.: Learning in the cultural process. In: Langton, C., et al. (eds.) Artificial Life II, pp. 689–706. MIT Press, Cambridge (1991)Google Scholar
  8. 8.
    Hutchins, E., Hazlehurst, B.: How to invent a lexicon: The development of shared symbols in interaction. In: Gilbert, N., Conte, R. (eds.) Artificial Societies: The Computer Simulation of Social Life, pp. 157–189. UCL Press, London (1995)Google Scholar
  9. 9.
    Kitano, H.: Designing neural networks using genetic algorithm with graph generation system. Complex Systems 4, 461–476 (1990)zbMATHGoogle Scholar
  10. 10.
    MacLennan, B., Burghardt, G.: Synthetic ethology and the evolution of cooperative communication. Adaptive Behavior 2(2), 161–188 (1993)CrossRefGoogle Scholar
  11. 11.
    Menczer, F.: Changing latent energy environments: A case for the evolution of plasticity. Technical Report CS94-336 (1994)Google Scholar
  12. 12.
    Miller, G.F., Todd, P.M., Hedge, S.U.: Designing neural networks using genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, pp. 379–384 (1989)Google Scholar
  13. 13.
    Nolfi, S., Parisi, D.: Learning to adapt to changing environments in evolving neural networks. Technical Report 95-15, Institute of Psychology, National Research Council, Rome, Italy (1995)Google Scholar
  14. 14.
    Opitz, D.W., Shavlik, J.W.: Generating accurate and diverse members of a neural-network ensemble. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 535–541. The MIT Press, Cambridge (1996)Google Scholar
  15. 15.
    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
  16. 16.
    Spector, L.: Genetic programming and AI planning systems. In: Proceedings of Twelfth National Conference on Artificial Intelligence, Seattle, Washington, USA, pp. 1329–1334. AAAI Press/MIT Press (1994)Google Scholar
  17. 17.
    Steels, L.: The synthetic modeling of language origins. In: Evolution of Communication, pp. 1–34 (1997)Google Scholar
  18. 18.
    Yao, X., Liu, Y., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)CrossRefGoogle Scholar
  19. 19.
    Yanco, H., Stein, L.: An adaptive communication protocol for cooperating mobile robots. In: From Animals to Animats 2. Proceedings of the second International Conference on Simulation of Adaptive Behavior, pp. 478–485. MIT Press, Cambridge (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dara Curran
    • 1
  • Colm O’Riordan
    • 1
  1. 1.National University of IrelandGalwayIreland

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