The Hybrid Genetic Algorithm for Blind Signal Separation

  • Wen-Jye Shyr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.


Genetic Algorithm Local Search Independent Component Analysis Finite Impulse Response Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wen-Jye Shyr
    • 1
  1. 1.Department of Industrial Education and TechnologyNational Changhua University of EducationChanghuaTaiwan, R.O.C.

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