Abstract:
Support vector machine (SVM) is originally developed for linear two-class classification via constructing an optimal separating hyper plane, where the margin is maximal. In case of not linearly separable training data, SVM is by means of kernel trick to map the original input space into a high dimensional feature space to enhance the classifier generalization ability. Genetic Algorithm (GA) is a stochastic and heuristic searching algorithm that is inspired by natural evolution. In the evolution, the candidate solutions are encoded to a group of strings (called chromosomes) by some kind of encoding method. Based on Darwin’s principle of ‘survival of the fittest’, the optimal candidate solution is obtained after a series of iterative GA computations. In each process of iteration (called generation), the GA consists of the elementary operation of Selection, Crossover and Mutation. In a GA, the fitness function is used to evaluate the quality of each individual comes out of the chromosomes. Individuals with high fitness are easier to be inherited to the next generation.By using GA along with SVM here we are trying to make classification of the objects such that it will be closer to the original image. This is simple effort to make identification easier.

Keywords: Support vector machine (SVM), Genetic Algorithm (GA)