Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms

Thomas Riechmann

Hannover Economic Papers (HEP) from Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät

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. 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.

Keywords: Learning; Computational economics; Genetic algorithms; Markov process; Evolutionary dynamics (search for similar items in EconPapers)
JEL-codes: C63 C73 D83 (search for similar items in EconPapers)
Pages: 22 pages
Date: 1997-10
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Related works:
Journal Article: Learning and behavioral stability An economic interpretation of genetic algorithms (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:han:dpaper:dp-209

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