Analysis of contrast functions in a genetic algorithm for post-nonlinear blind source separation

The task of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed is called the blind source separation problem. If the assumption in order to obtain the original sources is their statistical independence, then ICA (Independent Component Analysis) maybe the technique to recover the signals. In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. A thorough analysis of the performance of the chosen contrast functions is made by means of ANOVA (Analysis of Variance), showing the validity of the three approaches.