A novel Blind Source Separation(BSS) algorithm based on the combination of genetic algorithm and Independent Component Analysis (ICA) is proposed with analysis to the ICA method. The proposed algorithm can be used to solve the problem of local optimum that is easily stacked into by normal numerical solution. In the genetic algorithm

the Kurtosis as the fitness function is adopted

the elitist model is introduced and supplying filial generation's individual with migrant operation dynamically is also adopted. The simulation 1 is the separation of the mixed signals of three images and a noise. The simulation 2 is the separation of the mixed signals of two image signals (sub-gauss signal) and two voice signals (super-gauss signal). The image separation simulation shows that the blind signals separation can be realized and the global optimum can be acquired through the proposed algorithm under the circumstance of adequate population size and genetic generations. Compared with the Blind Source Separation method of extended-infomax

the proposed method in this paper can acquire better separating effect in separating the mixed signals of sub-gauss signal and super-gauss signal.