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A variable step size algorithm using evolution strategies for adaptive filtering

A new variable step size scheme for the least-mean-square (LMS) algorithm is proposed in this paper. The idea is basically a kind of evolutionary strategies. The step size candidates are generated and evaluated by calculating a square error measure based on a priori and a posteriori errors. The fittest candidate is selected for subsequent adaptation. The composition of the square error measure is regulated according to the mean square error so as to provide fast converging and tracking capability. The convergence performance is significantly improved and is less sensitive to eigenvalue spread.