Learning and behavioral stability - An economic interpretation of genetic algorithms

This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in math ematics and closely related sciences in order to find out more about the pr operties 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 of what an economist can learn from the mathematical branch of research, especially concerning the conver gence and stability properties of the genetic algorithm. It is shown that genetic algorithm learning is a compound of three differen t learning schemes. First, each particular scheme is analyzed. Then it is s hown that it is the combination of the three schemes that gives genetic alg orithm learning its special flair: A kind of stability somewhere in between asymptotic convergence and explosion.