The separation of Economic versus EA parameters in EA-learning

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. Current economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. In this paper we show that this practice may hinder the performance of the GA and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing two widely used approaches to population learning with respect to their convergence properties and robustness. 2000 Mathematics Subject Classification: 91-08