Convergence behavior of evolutionary digital filters on a multiple-peak surface

The authors have already proposed evolutionary digital filters (EDFs). 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 output of the EDF is the output of an inner filter of which fitness is maximum in the population. The adaptive algorithm of EDFs controls and changes the coefficients of inner filters using the cloning method (the asexual reproduction method) or the mating method (the sexual reproduction method). This adaptive algorithm of EDFs is a non-gradient and multi-point search algorithm. Thus, this algorithm is not susceptible to local minimum problems that arise from a multiple-peak surface. This paper shows the effectiveness and features of the EDF on a multiple-peak performance surface of system identifications. The EDF can search the global minimum in the multiple-peak performance surface of these examples