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Meta-Heuristics hybridizing independent component analysis with genetic algorithms

Górriz, J. M. ; Puntonet, Carlos G. ; Martin-Clemente, R. ; Lang, Elmar



Abstract

In this work we present a novel method for blindly separating unobservable independent component signals from their linear mixtures, using meta- heuristics such as genetic algorithms (GA) to minimize the nonconvex and nonlinear cost functions. This approach is very useful in many fields such as forecasting indexes in financial stock markets where the search for independent components is the ...

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