Optimizing blind source separation with guided genetic algorithms

This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its application to the problem of blind source separation (BSS) on post-nonlinear mixtures. The paper also includes a formal proof on the convergence of the proposed algorithm using guiding operators, a new concept in the GA scenario. This approach is very useful in many fields such as forecasting indexes in financial stock markets, where the search for independent components is the major task to include exogenous information into the learning machine; or biomedical applications which usually use a high number of input signals. The guiding GA (GGA) presented in this work, is able to extract IC with faster rate than the previous ICA algorithms, as input space dimension increases. It shows significant accuracy and robustness than the previous approaches in any case. In addition, we present a simple though effective contrast...

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: J. M. Górriz, Carlos García Puntonet, Fernando Rojas, R. Martin, S. Hornillo, Elmar Wolfgang Lang
• Keywords: Blind Source Separation, IJON 2006, Independent Components, Unobservable Independent Component