Exchange Rate Forecasting with Combined Genetic Algorithms

In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of business, finance, medical science and engineering. In this study, we utilize a Genetic Algorithm (GA) to select the optimal variable weights in order to predict exchange rates using Genetic Algorithms, Particle Swam Optimization (PSO) and Back Propagation Network (BPN). Then, we construct three models: GA_GA, GA_PSO, and GA_BPN. Fundamentally, we expect enhanced variable selection to provide improved forecasting performance. The results of our experiments indicate that the GA_GA model achieves the best forecasting performance and is highly consistent with the actual data.