Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/78324
Authors: 
Riechmann, Thomas
Year of Publication: 
1997
Series/Report no.: 
Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät, Universität Hannover 209
Abstract: 
This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other hand, recent economic literature uses genetic algorithms as a metaphor for social learning. This paper will face the question what an economist can learn from the mathematical branch of research, especially concerning the convergence and stability properties of the genetic algorithm.<br />It is shown that genetic algorithm learning is a compound of three different learning schemes. First, every particular scheme is analyzed. Then it will be pointed out that it is the combination of the three schemes that gives genetic algorithm learning its special flair: A kind of stability somewhere in between asymptotic convergence and explosion.
Subjects: 
Learning
Computational economics
Genetic algorithms
Markov process
Evolutionary dynamics
JEL: 
C63
C73
D83
Document Type: 
Working Paper

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