Genetic and annealing approaches to adaptive digital filtering

Novel approaches to adaptive digital filtering based on genetic algorithms (GAs) and simulated annealing (SA) are proposed. Algorithms based on using the gradient of the mean square error or on least-square principles have been found to have inadequacies when adapting IIR filters. The process of adaptation can be cast as an optimization problem. GAs are used in the context of multiparameter optimization. Simulation results are presented to show how these approaches are able to tackle the problems of global optimality and dimensionality when adapting high-order IIR filters. Hybrid schemes where concepts of SA are incorporated into GAs are proposed