Affiliation:
1. LEN, Nantes University, France
Abstract
The goal of this chapter is twofold. First, assuming that all agents belong to a genetic population, the evolution of inflation learning will be studied using a heterogeneous genetic learning process. Second, by using real-floating-point coding and different genetic operators, the quality of the learning tools and their possible impact on the learning process will be examined.
Reference40 articles.
1. The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies
2. Evolutionary dynamics of currency substitution
3. Axelrod, R. (1987). The evolution of strategies in the iterated prisoner’s dilemma. In L. D. Davis (Ed.), Genetic algorithms and simulated annealing. San Francisco: Morgan Kaufmann.
4. Inflation and reputation.;D.Backus;The American Economic Review,1985
5. A Positive Theory of Monetary Policy in a Natural Rate Model