Elitist genetic algorithm guided by higher order statistic for blind separation of digital signals

A novel method for blind separation of digital signals based on elitist genetic algorithms is presented in this paper. Contrast function, consisting in a weighted sum of high order statistics measures (cumulants of different orders), plays the role of genetic fitness function, and also guide the genetic algorithm by a Gauss-Newton adaptation applied to the genetic population, that reduces the search space and provide faster convergence rate. The use of elitism assures the convergence of the algorithm. Several experiments were conducted on digital signals and mixing models, and the high amount of simulations derived from them provided the best combination of the constant parameters in terms of separation accuracy and convergence rate. In this sense, we also achieve a robust blind source separation method that efficiently adapts to the statistical nature of the mixing signals, within a low population of the genetic algorithm.