Genetic algorithm learning and evolutionary games

This paper links 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 an 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 to finally approach a neighborhood of an evolutionarily stable state. In order to characterize this kind of dynamics, a concept of evolutionary superiority and evolutionary stability of genetic populations is developed, which allows for a comprehensive analysis of the evolutionary dynamics of the standard GA learning processes. (C) 2001 Elsevier Science B.V. All rights reserved.