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Genetic Algorithms as Optimalisation Procedures


  • Sándor Karajz

    () (University of Miskolc)


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.

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  • 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

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    References listed on IDEAS

    1. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    2. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    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.
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