Guided GA-ICA algorithms

Authors:
Juan Manuel Górriz;Carlos García Puntonet;Angel Manuel Gómez;Oscar Pernía
Affiliations:
Dept. Signal Theory. Facultad de Ciencias, Universidad de Granada, Granada, Spain;Dept. Signal Theory. Facultad de Ciencias, Universidad de Granada, Granada, Spain;Dept. Signal Theory. Facultad de Ciencias, Universidad de Granada, Granada, Spain;Dept. Signal Theory. Facultad de Ciencias, Universidad de Granada, Granada, Spain
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - 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 components from their linear mixtures, uses genetic algorithms (GA) to find the separation matrices which minimize a cumulant based contrast function. We focuss our attention on theoretical analysis of convergence including a formal prove on the convergence of the well-known GA-ICA algorithms. In addition we introduce guiding operators, a new concept in the genetic algorithms scenario, which formalize elitist strategies. 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 Guided GA (GGA) presented in this work converges to uniform populations containing just one individual, the optimum.