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Genetic algorithms in evolutionary modelling

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Author Info

  • Chris Birchenhall

    (School of Economic Studies, PREST and CRIC, University of Manchester, Oxford Road, Manchester M13 9PL, UK)

  • Nikos Kastrinos

    (School of Economic Studies, PREST and CRIC, University of Manchester, Oxford Road, Manchester M13 9PL, UK)

  • Stan Metcalfe

    (School of Economic Studies, PREST and CRIC, University of Manchester, Oxford Road, Manchester M13 9PL, UK)

Abstract

Evolutionary modellers have recently taken an interest in the use of computer simulations based on genetic algorithms; this paper offers two contributions to this literature. In the initial sections we aim to place the GA into a general review of evolutionary dynamics, including Fisher's Principle. In the second half of the paper, we offer a modified GA that replaces the selection and crossover operators with a selective transfer operator. We argue this modified algorithm has a ready interpretation in the modelling of learning, namely as a proxy for imitation in a population working with modular technologies. A simple application is used to give an initial assessment of the algorithm and to test Fisher's Principle.

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Bibliographic Info

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

Volume (Year): 7 (1997)
Issue (Month): 4 ()
Pages: 375-393

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Handle: RePEc:spr:joevec:v:7:y:1997:i:4:p:375-393

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Related research

Keywords: Genetic algorithms ; Competition ; Evolutionary dynamics ; Population learning;

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Cited by:
  1. Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
  2. Riechmann, Thomas, 2000. "A Model of Boundedly Rational Consumer Choice - An Agent Based Appraoch," Hannover Economic Papers (HEP) dp-232, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
  4. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
  5. Windrum,Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
  6. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Beyond replicator dynamics: Innovation-selection dynamics and optimal diversity," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 229-245, May.
  7. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
  8. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
  9. Christiane Clemens & Thomas Riechmann, 2006. "Evolutionary Dynamics in Public Good Games," Computational Economics, Society for Computational Economics, vol. 28(4), pages 399-420, November.
  10. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
  11. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.

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