Blind Source Separation Based On Evolutionary Algorithms

Blind Source Separation (BSS) is an approach to estimate and recover the independent source signals by using only the information within the mixtures observed at each input channel. One of the key issues in BSS is to develop the learning algorithm for the decomposition of matrices. Many approaches have been proposed to solve this problem, such as stochastic gradient algorithm based on information entropy method, natural gradient by maximum likelihood estimation based on information entropy method and so on. However, these approaches can only be applied to the scenairo in which sub-Gaussian source and super-Gaussian source signals existed solely. By these approaches, the convergence is lower and there are uncertain properties. JADE (Joint Approximate decomposition of Eigen matrices) algorithm is based on 4th-order cumulation, which is a typical algorithm of independent component analysis(ICA). JADE can separate blind signals in many cases. Unfortunately, it can't get accurate solutions when K>2, which K is the size of BSS problem. This paper studies the algorithms of blind source separation based on evolution algorithm. The main works in this paper are as follows:The simple Generic Algorithm (GA) is applied to the problem of blind source separation. The key idea is to use GA instead of the joint diagonalization operation in JADE in order to improve the accurateness of the solution. It's well known that genetic algorithm can search the optimum solution from the whole space of the solutions, not only for big population and multiple peak value but also nonlinearity and the global optimum. The theoretical analyses and experimental results show that the algorithm of blind source separation based on genetic algorithm has better convergence, higher precision of separation. Also, the algorithm is superior to JADE algorithm and has stronger robustness.Due to the linkage problem accounted by the recombination operator of genetic algorithm and the existence of Gauss signals in resource signals, punishment functions must be used which may cause vibration in the process of resolving solutions. That will cause worse separation effects. To deal with this problem, an algorithm of the blind source separation based on Bayesian with decision graphs is proposed, which adopts Bayesian with decision graphs instead of joint diagonalization operation of JADE. The algorithm based on Bayesian with decision graphs collects the information from the optimum sets, and then generates the new solutions by the distribution of prior information. It can deal with the linkage problem generated by the traditional recombination and mutation operators in simple GA. Therefore the blind source separation algorithm based on Bayesian withdecision graphs has better performance of convergence and higher precision of separation.In order to broaden the application fields of proposed algorithms, in this paper, a robust watermarking method based on genetic algorithm is proposed. It guarantees better visual effect by using iterative blending to embed watermark. Moreover it makes algorithm resist geometric attacks effectively by using blind source separation method based on genetic algorithm to detect watermarking without knowing the position of embedding and detecting. The theoretical analyses and experimental results show that this method is good robust and feasible.