Applying Neural Networks and Genetic Algorithms to the Separation of Sources

This paper presents a new adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated annealing (SA) and competitive learning (CL) methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple linearization in the mixture space. Also, the paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). From experimental results, this paper demonstrates the possible benefits offered by GAs in combination with BSS, such as robustness against local minima, the parallel search for various solutions, and a high degree of flexibility in the evaluation function. The main characteristics of the method are its simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

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