Guided GA-ICA Algorithms

摘要

In this paper we present a novel GA-ICA method which converges to the optimum. The new method for blindly separating unobserv-able independent components from their linear mixtures, uses genetic algorithms (GA) to find the separation matrices which minimize a cu-mulant 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.