Genetic algorithms in evolutionary modelling
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.
If 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 7 (1997)
Issue (Month): 4 ()
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/economics/journal/191/PS2|
When requesting a correction, please mention this item's handle: RePEc:spr:joevec:v:7:y:1997:i:4:p:375-393. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If references are entirely missing, you can add them using this form.