Plugging an histogram-based contrast function on a genetic algorithm for solving postnonlinear-BSS

Abstract

This paper proposes a novel Independent Component Analysis algorithm based on the use of a genetic algorithm intended for its application to the problem of blind source separation on post-nonlinear mixtures. We present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based on estimating the probability densities of the outputs through histogram approximation. Although more sophisticate methods for probability density function approximation exist, such as kernelbased methods or k -nearest-neighbor estimation, the histogram presents the advantage of its simplicity and easy calculation if an appropriate number of samples is available. © Springer-Verlag 2004.