In this paper, we compare the performance of evolutionary digital filters (EDFs) for IIR adaptive digital filters (ADFs) in terms of convergence behavior and stability, and discuss their advantages. The authors have already proposed the EDF which is controlled by adaptive algorithm based on the evolutionary strategies of living things. This adaptive algorithm of the EDF controls and changes the coefficients of inner digital filters using the cloning method or the mating method. Thus, the adaptive algorithm of the EDF is of a non-gradient and multi-point search type. Numerical examples are given to demonstrate the effectiveness and features of the EDF such that (1) they can work as adaptive filters as expected, (2) they can adopt various error functions such as the mean square error, the absolute sum error, and the maximum error functions, and (3) the EDF using IIR filters (IIR-EDF) has a higher convergence rate and smaller adaptation noise than the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface.

This letter proposes evolutionary digital filters (EDFs) as new adaptive digital filters. The EDF is an adaptive filter which is controlled by adaptive algorithm based on the evolutionary strategies of living things. It consists of many linear/time-variant inner digital filters which correspond to individuals. The adaptive algorithm of the EDF controls and changes the coefficients of inner filters using the cloning method (the asexual reproduction method) or the mating method (the sexual reproduction method). Thus, the search algorithm of the EDF is a non-gradient and multi-point search algorithm. Numerical examples are given to show the effectiveness and features of the EDF such that they are not susceptible to local minimum in the multiple-peak performance surface.