Signal separation by independent component analysis based on a genetic algorithm

We propose a genetic algorithm for blind source separation (BSS). The BSS problem is to obtain the independent components of original source signals from mixed signals. The original sources that are mutually independent and are mixed linearly by an unknown matrix are retrieved by a separating procedure using independent component analysis (ICA). The goal of ICA is to find a separating matrix so that the separated signals are as independent as possible. Many neural learning algorithms for minimizing the dependency among signals have been proposed for obtaining the separating matrix. The effectiveness of these algorithms, however, is affected by the neuron activation functions that depend on the probability distribution of the signals. In our method, the separating matrix is evolved by a genetic algorithm (GA) that does not need activation functions and works on an evolutionary mechanism. The kurtosis that is a simple and original criterion for independence is used in the fitness function of GA. The applicability of the proposed method for blind source separation is demonstrated by the simulation results.