Techniques based on biometrics have been successfully applied to personal identification systems. One rather promising technique uses the keystroke dynamics of each user in order to recognize him/her. In the present study, we present the development of a hybrid system based on support vector machines and stochastic optimization techniques. The main objective is the analysis of these optimization algorithms for feature selection. We evaluate two optimization techniques for this task. genetic algorithms (GA) and particle swarm optimization (PSO). We use the standard GA and we created a PSO variation, where each particle is represented by a vector of probabilities that indicate the possibility of selecting a particular feature and directly affects the original values of the features. In the present study, PSO outperformed GA with regard to classification error, processing time and feature reduction rate.
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