Fast implementation of Analytic Programming used for large amount of datasets

Abstract

The aim of this master thesis is to discuss a method useful for spectra analysis -- analytical programming and its fast implementation. My goal is to create mathematical formulas of emission lines from spectra, which are characteristic for Be stars. One issue in performing this task is symbolic regression, which represents the process in our application, when measured data fit the best represented mathematical formula. In past this was only a human domain; nowadays, there are computer methods, which allow us to do it more or less effectively. A novel method in symbolic regression, compared to genetic programming and grammatical evolution, is analytic programming. The aim of this work is to verify the efficiency of the parallel approach of this algorithm, using CUDA architecture, which can be run on a server. Next I will discuss implementation of random decision forest to classify huge amounts of various spectra with the help of mathematical functions obtained via analytical programming, as shown in small example.