Signal separation method using genetic algorithm

Some noise reduction methods are based on minimizing the dependence among input signals to separate a noise component, because a noise component is usually independent on the other signals. We have developed a new method to separate a noise component which directly minimizes the Kullback-Leibler divergence by a genetic algorithm (GA). The Kullback-Leibler divergence is lower when input signals have lower dependence from each other. Therefore, finding the transformation of input signals which minimizes this measure is equivalent to separate independent noise components from the noise mixed input signals. We have adopted a genetic algorithm to minimize the Kullback-Leibler divergence. GA is one of parallel processing optimization methods, which imitates biological genes and is suitable for random optimization problems, Finally, we have performed computer simulations to evaluate the developed method. Results of initial simulations show that the method is promising