摘要：Genetic algorithms (GAs) are computer programs that mimic the processes of biological evolution in order to solve problems and to model evolutionary systems. In this study, we apply GAs for technical models of exchange rate determination in exchange rate market. In this framework, we estimated auto regressive (AR), moving average (MA), auto regressive with moving average (ARMA) and mean reversion (MR) as technical models for the Iran’s Rial against the European Union’s (EU) Euro (Rial/Euro) using monthly data from January 1992 to December 2008. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria; R-squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Results showed that for explanation of the Iran’s Rial against the European Union’s Euro exchange rate behavior, auto regressive (AR) and auto regressive with moving average (ARMA) are better than other technical models.
