Genetic Algorithms as Optimalisation Procedures
AbstractDrawing 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Faculty of Economics, University of Miskolc in its journal Theory Methodology Practice (TMP).
Volume (Year): 4 (2007)
Issue (Month): 01 ()
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Thomas Riechmann, 1999.
"Learning and behavioral stability An economic interpretation of genetic algorithms,"
Journal of Evolutionary Economics,
Springer, vol. 9(2), pages 225-242.
- Riechmann, Thomas, 1997. "Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms," Hannover Economic Papers (HEP) dp-209, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393.
- Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
- Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Society for Computational Economics, vol. 8(3), pages 233-53, August.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.