Independent component analysis using a genetic algorithm
David B. Hillis, Brian M. Sadler, Ananthram Swami · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
The independent component analysis (ICA) problem involves finding a set of statistically independent signals from a set of measurements consisting of unknown, perhaps convolutive mixtures of those signals. This problem arises in many applications such as speech processing, communications, and biomedical signal processing. We present a method to blindly separate instantaneous mixtures of non- Gaussian signals using a genetic algorithm (GA) and higher order statistics. The GA searches for a separating matrix such that the resulting output signal are both statistically independent and strongly non-Gaussian as measured by the kurtosis. The GA uses a binary representation together with a coarse-to-fine strategy to speed convergence and avoid such bits. Using data from a simulated narrow band communications scenario, we examine the algorithm's performance as signal length and sensor noise level are varied. We compare this performance with that obtained using the ACI algorithm developed by Comon. We show that the GA is able to achieve good separation of dense signal constellations, and achieves better separation with lower mean-square estimation error than the ACI, albeit with much higher algorithmic complexity. The improvement in performance may be dramatic when the signal length is short.