A new variable step-size algorithm using genetic-type search

This paper proposes a new variable step-size algorithm using an evolutionary-type searching approach. The step-sizes of the adaptive filter are evolved regularly in a controlled manner to reduce the squared estimation error at each iteration. The algorithm improves the rate of convergence and achieves a low steady-state misadjustment. The performance analysis of the adaptive filter and simulation results are presented in the paper. The results show that the new algorithm outperforms the least mean square (LMS) algorithm with constant step-size considerably