Constrained genetic algorithm based independent component analysis

Independent component analysis, a computationally efficient statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper proposes a constrained genetic algorithm optimization based independent component analysis assuming a noise free independent component analysis (ICA) model. It investigates on the application and performance of the popular evolutionary computation technique GA in independent component analysis problem. It is observed that the proposed constrained genetic algorithm optimization based ICA overcomes the long standing permutation ambiguity and recovers the independent components in a fixed order which is dependent on the statistical characteristics of the signals to be estimated. The constrained GA based ICA has also been compared with the most popular fast ICA algorithm.