Genetic Algorithm Optimization of Dynamic Support Vector Regression

Abstract

We show that genetic algorithms (GA) find optimized dynamic support vector machines (DSVMs) more efficiently than the grid search (GS) optimization approach. In addition, we show that GA-DSVMs find extremely low-error solutions for a number of oft-cited benchmarks. Unlike standard support vector machines, DSVMs account for the fact that data further back in a time series are generally less predictive than more-recent data. In order to tune the discounting factors, DSVMs require two new free parameters for a total of five. Because of the five free parameters, traditional GS optimization becomes intractable for even modest grid resolutions. GA optimization finds better results while using fewer computational resources.