Nonlinear blind source separation using a genetic algorithm

Demixing independent source signals from their nonlinear mixtures is a very important issue in many scenarios. This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, high robustness, and high convergence rate. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model.