Evolutionary algorithm using mutual information for independent component analysis

Independent Component Analysis (ICA) is a method for finding underlying factors from multidimensional statistical data. ICA differs from other similar methods in that it looks for components that are both statistically independent and nongaussian. Blind Source Separation (BSS) consists in recovering unobserved signals from a known set of mixtures. Thus, ICA and BSS are equivalent when the mixture is assumed to be linear up to possible permutations and invertible scalings. However, when the mixing model is nonlinear, additional constraints are needed to assure that independent components correspond to the original signals. In this paper, we propose a simple though effective method based on estimating the probability densities of the outputs for solving the BSS problem in linear and nonlinear mixtures making use of genetic algorithms. A post-nonlinear mixture model is assumed so that the solution space in the nonlinear case is restricted to signals equivalent to the original ones.