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Can evolutionary algorithms describe learning processes?

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Author Info

  • Thomas Brenner

    () (Max-Planck-Institute for Research into Economic Systems, Evolutionary Economics Unit, Kahlaische Strasse 10, D-07745 Jena, Germany)

Abstract

Evolutionary algorithms have attracted more and more the attention of economists in recent years. Repeatedly it is claimed that they are an adequate tool to describe learning processes within a population of individuals. The present paper examines this claim. To this end, a learning model is set up that contains the three elements of variation, elimination, and imitation that are claimed to correspond with the processes of mutation, selection, and replication of biological evolution. Subsequently, this model is compared with a formulation of evolutionary algorithms. The comparison reveals that although both processes have a similar structure there are crucial differences between the two dynamics.

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Bibliographic Info

Article provided by Springer in its journal Journal of Evolutionary Economics.

Volume (Year): 8 (1998)
Issue (Month): 3 ()
Pages: 271-283

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Handle: RePEc:spr:joevec:v:8:y:1998:i:3:p:271-283

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Related research

Keywords: Social evolution ; Evolutionary algorithms ; Learning;

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Cited by:
  1. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
  2. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
  3. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
  4. Jérome VICENTE (GRES-LEREPS), 2003. "From interaction economics to economic geography : theories and evidences (In French)," Cahiers du GRES 2003-02, Groupement de Recherches Economiques et Sociales.
  5. Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999 343, Society for Computational Economics.
  6. Fent, Thomas, 1999. "Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market," MPRA Paper 2837, University Library of Munich, Germany.
  7. Markus Pasche, 2005. "Das Vertrauensspiel - eine verhaltensorientierte Erkl�rung," Jenaer Schriften zur Wirtschaftswissenschaft 19/2005, Friedrich-Schiller-Universität Jena, Wirtschaftswissenschaftliche Fakultät.

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