Simulated annealing Based-GA using injective contrast functions for BSS

Authors:
J. M. Górriz;C. G. Puntonet;J. D. Morales;J. J. delaRosa
Affiliations:
Facultad de Ciencias, Universidad de Granada, Granada, Spain;Facultad de Ciencias, Universidad de Granada, Granada, Spain;Facultad de Ciencias, Universidad de Granada, Granada, Spain;Facultad de Ciencias, Universidad de Granada, Granada, Spain
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Year:
2005

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Abstract

In this paper we present a novel GA-ICA method which converges to the optimum. The new method for blindly separating unobservable independent component signals from their linear mixtures (Blind Source Separation BSS), uses genetic algorithms (GA) to find the separation matrices which minimize a cumulant based contrast function. The paper also include a formal prove on the convergence of the proposed algorithm using guiding operators, a new concept in the genetic algorithms scenario. This approach is very useful in many fields such as biomedical applications i.e. EEG which usually use a high number of input signals. The Guiding GA (GGA) presented in this work converges to uniform populations containing just one individual, the optimum.