Gabor filter subset selection using a genetic algorithm

This paper introduces a hybrid methodology that ensemble genetic algorithms (GAs) and Support Vector Machine (SVM) in order to evolve optimal subsets of Gabor filters for efficient pattern classification. Although some filter design procedure are available for Gabor filters, high computations are needed and the efficiency of design is dependent on the particular Gabor filters subset. In this paper to reduce the computational cost and improve the performance, a GA is used to search the space of all possible subsets of a large pool of Gabor candidate filters. The classification performance of SVM, an unknown data, together with filtering cost are used as measure of fitness that is used as feedback by GA to evolve better Gabor filter sets. This assembled system iterates until filters subset is found with a satisfactory classification performance and a significant reduced filters number.