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Genetic algorithms in evolutionary modelling

Author

Listed:
  • 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.

Suggested Citation

  • Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393.
    • Handle: RePEc:spr:joevec:v:7:y:1997:i:4:p:375-393
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    Citations

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    Cited by:

    1. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
    2. 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.
    3. Quan, Ji & Dong, Xu & Wang, Xianjia, 2022. "Rational conformity behavior in social learning promotes cooperation in spatial public goods game," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    4. Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
    5. Christiane Clemens & Thomas Riechmann, 2006. "Evolutionary Dynamics in Public Good Games," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 399-420, November.
    6. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    7. 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.
    8. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    9. 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.
    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. Quan, Ji & Zhou, Yawen & Wang, Xianjia & Yang, Jian-Bo, 2020. "Information fusion based on reputation and payoff promotes cooperation in spatial public goods game," Applied Mathematics and Computation, Elsevier, vol. 368(C).
    12. Quan, Ji & Zhou, Yawen & Wang, Xianjia & Yang, Jian-Bo, 2020. "Evidential reasoning based on imitation and aspiration information in strategy learning promotes cooperation in optional spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    13. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    14. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).

    More about this item

    Keywords

    Genetic algorithms ; Competition ; Evolutionary dynamics ; Population learning;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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