Milnes, Thomas BradfordThorpe, Christopher AndrewPfeffer, Avi2016-01-212009Milnes, Thomas Bradford, Christopher Thorpe, and Avi Pfeffer. 2009. Genetic Algorithm Optimization of Dynamic Support Vector Regression. Harvard Computer Science Group Technical Report TR-08-09.http://nrs.harvard.edu/urn-3:HUL.InstRepos:24825702We 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.en-USResearch Paper or Report2016-01-21