IDEAS home Printed from https://ideas.repec.org/a/mic/tmpjrn/v4y2007i01p37-41.html
   My bibliography  Save this article

Genetic Algorithms as Optimalisation Procedures

Author

Listed:
  • Sándor Karajz

    (University of Miskolc)

Abstract

Drawing a parallel between biological and economic evolution provides an opportunity for the description of dynamic economic processes changing in time by using genetic algorithms. The first step in finding algorithms in biological and economic processes is to draw a parallel between the terms used in both disciplines and to determine the degree of elaboration of analogues. On the basis of these ideas it can be stated that most biological terms can be used both in economics and in the social field, which satisfies the essential condition for successful modeling. Genetic algorithms are derived on the basis of Darwin-type biological evolution and the process starts from a possible state (population), in most cases chosen at random. New generations emerge from this starting generation on the basis of various procedures. These generating procedures go on until the best solution to the problem is found. Selection, recombination and mutation are the most important genetic procedures.

Suggested Citation

  • Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
  • Handle: RePEc:mic:tmpjrn:v:4:y:2007:i:01:p:37-41
    as

    Download full text from publisher

    File URL: http://tmp.gtk.uni-miskolc.hu/volumes/2007/01/TMP_2007_01_06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    2. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    3. Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 8(3), pages 233-253, August.
    4. Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    2. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    3. Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
    4. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    5. 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.
    6. repec:dgr:rugsom:99b41 is not listed on IDEAS
    7. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
    8. 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.
    9. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    10. Maria Minniti & William Bygrave, 2001. "A Dynamic Model of Entrepreneurial Learning," Entrepreneurship Theory and Practice, , vol. 25(3), pages 5-16, April.
    11. Roos, Michael W. M., 2015. "The macroeconomics of radical uncertainty," Ruhr Economic Papers 592, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    12. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    13. 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.
    14. David van Bragt & Han La Poutré, 2001. "Evolving Automata Play the Alternating-Offers Game," CeNDEF Workshop Papers, January 2001 2B.3, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    15. Tesfatsion, Leigh, 1998. "Teaching Agent-Based Computational Economics To Graduate Students," Economic Reports 18193, Iowa State University, Department of Economics.
    16. van Bragt, David & van Kemenade, Cees & la Poutre, Han, 2001. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 253-263, June.
    17. Thomas Riechmann, 2006. "Cournot or Walras? Long-Run Results in Oligopoly Games," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 162(4), pages 702-720, December.
    18. Theo S Eicher & Klaas vant Veld, 2000. "Search in Research: An Evolutionary Approach to Technical Change and Growth"," Discussion Papers in Economics at the University of Washington 0005, Department of Economics at the University of Washington.
    19. 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).
    20. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    21. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mic:tmpjrn:v:4:y:2007:i:01:p:37-41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/vgtmihu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.