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Blind signals separation with genetic algorithm and particle swarm optimization based on mutual information

Radioelectronics and Communications Systems (Impact Factor: 0.19). 06/2011; 54(6):315-324. DOI: 10.3103/S0735272711060045

ABSTRACT Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper,
we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In
these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In
order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sources.
Simulations results demonstrate that the proposed method for employing fitness function has rapid convergence, simplicity
and a more favorable signal to noise ratio for separation tasks based on particle swarm optimization and continuous genetic
algorithm than binary genetic algorithm. Also, particle swarm optimization enjoys shorter computation time than the other
two algorithms for solving these optimization problems for multiple sources.

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