New independent component analysis method based on improved genetic algorithm

The performance of existing independent component analysis methods is highly affected by the non-linear contrast functions that are selected according to the distribution of original signals, the separation results are not always ideal, especially for the mixture of super-Gaussian signal and sub-Gaussian signal. To solve this problem, a new independent component analysis method based on improved genetic algorithm was proposed, where the probability of separated signals was estimated by histogram method, so the mutual entropy could be easily evaluated, then genetic algorithm was applied to find the separation matrix to minimize the mutual entropy. At the same time, an improved genetic algorithm was proposed to overcome some shortcomings of standard genetic algorithm, such as poor local searching ability and premature convergence. Simulation results show that the proposed independent component analysis method is superior to FastICA in separating the mixture of super-Gaussian signal and sub-Gaussian signal. Simulation results also prove the optimization ability of improved genetic algorithm.