A genetic algorithm for blind source separation based on independent component analysis


Gokongwei College of Engineering


Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2014 - 7th HNICEM 2014 Joint with 6th International Symposium on Computational Intelligence and Intelligent Informatics, co-located with 10th ERDT Conference

Publication Date



This paper presents the implementation of genetic algorithm (GA) to a simple blind source separation(BSS) problem using independent component analysis(ICA). The process did not include pre-processing of mixture signals such as centering and whitening like most of ICA algorithms. The GA directly guesses the coefficients of the separating matrix given the mixture signals as inputs using maximization of kurtosis and minimization of mutual information as fitness function. Only one fitness function was defined to account the fitness for kurtosis and mutual information. Three set of simulations were performed. The first two simulations used the mixture of two and three synthetic signals, respectively. The third simulation used four audio signals. The results show that the proposed algorithm indeed separates the independent sources consisting of synthetic signals. The simulation consisting of four audio signals separates only three signals. It failed to extract one signal probably because the signal is almost a gaussian signal. © 2014 IEEE.


Digitial Object Identifier (DOI)



Electrical and Computer Engineering


Genetic algorithms; Blind source separation; independent component analysis

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