Abstract
The paper is concerned with genetic algorithm learning in a cobweb economy. Besides discussing several specification details in the genetic operators, the model includes four different types of firm forecasting rules and subjects the demand side to serially correlated random shocks. The main finding of the simulation experiments is that the genetic algorithm is a reasonably good approximation of the moving Walrasian equilibria, and that this process is characterized by the coevolution of different strategies. Accordingly, it is just the persistent heterogeneity of firms, and the persistently changing composition of this heterogeneity, that achieves stability. In this world, convergence is improved by weak, rather than strong, evolutionary pressure.
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