Distributed quadratic programming solver for kernel SVM using genetic algorithm

Support vector machine (SVM) is a powerful tool for classification and regression problems, however, its time and space complexities make it unsuitable for large datasets. In this paper, we present GeneticSVM, an evolutionary computing based distributed approach to find optimal solution of quadratic programming (QP) for kernel support vector machine. In Ge-neticSVM, novel encoding method and crossover operation help in obtaining the better solution. In order to train a SVM from large datasets, we distribute the training task over the graphics processing units (GPUs) enabled cluster. It leverages the benefit of the GPUs for large matrix multiplication. The experiments show better performance in terms of classification accuracy as well as computational time on standard datasets like GISETTE, ADULT, etc.