Genetic Algorithms and Economic Evolution
This paper tries to connect the theory of genetic-algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria, which, during the learning process, build up finally to reach a neighborhood of an evolutionarily stable state. In order to clarify this point, a concept of evolutionary stability of genetic populations is developed. It then becomes possible to explain the reasons for the dynamics of standard GA learning models as well as those of extensions to this standard.
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|Date of creation:||01 Mar 1999|
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Web page: http://fmwww.bc.edu/CEF99/
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