Optimizing blind source separation with guided genetic algorithms

  • Authors:

  • J. M. Górriz;C. G. Puntonet;F. Rojas;R. Martin;S. Hornillo;E. W. Lang

  • Affiliations:

  • Department Signal Theory and Communications, Facultad de Ciencias, University of Granada, Fuentenueva s/n 18071, Granada, Spain;Department Architecture and Computer Technology, E.T.S.I. Informática, University of Granada, Daniel Saucedo s/n 18071, Granada, Spain;Department Architecture and Computer Technology, E.T.S.I. Informática, University of Granada, Daniel Saucedo s/n 18071, Granada, Spain;Department of Signal Theory and Communications, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos 41092, Sevilla, Spain;Department of Signal Theory and Communications, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos 41092, Sevilla, Spain;Institute of Biophysics, University of Regensburg, Universitätsstraíe 31 D-93040 Regensburg, Germany

  • Venue:
  • Neurocomputing
  • Year:
  • 2006

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Abstract

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 function which evaluates individuals of each population (candidate solutions) based (a) on estimating the probability densities of the outputs through histogram approximation and (b) evaluating higher-order statistics of the outputs.