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Culture and the Baldwin Effect

  • Diego Federici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

Abstract

It is believed that the second phase of the Baldwin effect is basically governed by the cost of learning. In this paper we argue that when learning takes place the fitness landscape undergoes a modification that might block the Baldwin effect even if the cost of learning is high. The argument is that learning strategies will bias the evolutionary process towards individuals that genetically acquire better compared to individuals that genetically behave better. Once this process starts the probability of experiencing the Baldwin effect decreases dramatically, whatever the learning cost. A simulation with evolving learning individuals capable of communication is set to show this effect. The set of acquired behaviors (culture) competes with the instinctive one (genes) giving rise to a co-evolutionary effect.

Keywords

Local Search Operant Conditioning Social Strategy Culture Quality Communication Speed 
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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References

  1. 1.
    Baldwin, J.M.: A new factor in evolution. American Naturalist 30, 441–451 (1896)CrossRefGoogle Scholar
  2. 2.
    Downing, K.: Reinforced Genetic Programming. In: Genetic Programming & Evolvable Machines, pp. 259–288. Kluwer Publishers, Dordrecht (2001)Google Scholar
  3. 3.
    Turney, P.: Myths and legends of the Baldwin Effect. In: Proceedings of the ICML-1996 (13th International Conference on Machine Learning) pp.135–142 (1996)Google Scholar
  4. 4.
    Turney, P., Whitley, D., Anderson, R.: Evolution, learning and instict: 100 years of the Baldwin effect. Evolutionary Computation 4(3), 206–212 (1996)Google Scholar
  5. 5.
    Belew, R.K., Mitchell, M.: Adaptive individuals in evolving populations: models and algorithms. Addison-wesley, Reading (1996)Google Scholar
  6. 6.
    Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Systems 1, 495–502 (1987)zbMATHGoogle Scholar
  7. 7.
    Chalupa, L.M., Finlay, B.L.: Development and organization of the retina: from molecules to function. Plenum Press, New York (1998)Google Scholar
  8. 8.
    French, R., Messinger, A.: Genes, phenes and the Baldwin effect. In: Proceedings of Artificial Life IV, pp. 277–282 (1994)Google Scholar
  9. 9.
    Dawkins, R.: The selfish gene. Oxford university press, Oxford (1976)Google Scholar
  10. 10.
    Kandel, E.R., Schwartz, J.S., Jessell, T.M.: Principles of neural science, 4th edn. Elsevier Science Publishing Co., New York (2000)Google Scholar
  11. 11.
    Markram, H., L’bke, J., Frotscher, M., Sakmann, B.: Regulation of Synaptic Efficacy by Coincidence of Post-synaptic APs and EPSPs. Science 275, 213–215 (1997)CrossRefGoogle Scholar
  12. 12.
    Carew, T.J.: Behavioral Neurobiology: The Cellular Organization of Natural Behavior Sinauer Associates (2000)Google Scholar
  13. 13.
    Arita, T., Suzuki, R.: Interactions between learning and evolution: The outstanding strategy generated by the Baldwin effect. In: Proceedings of Artificial Life VII, pp. 196–205 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Diego Federici
    • 1
  1. 1.Department of computer and information scienceNorwegian University of Science and TechnologyTrondheimNorway

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