Genetic algorithm approach to nonlinear blind source separation

This paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA, and neural networks, has been amply discussed in the literature, the possibility of using genetic algorithms has not been explored thus far. However, in Nonlinear Mixtures, optimization of the system parameters and, especially, the search for invertible functions is very difficult due to the existence of many local minima. 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.