The genetic search approach. A new learning algorithm for adaptive IIR filtering

An “evolutionary” approach called the genetic algorithm (GA) was introduced for multimodal optimization in adaptive IIR filtering. However, the disadvantages of using such an algorithm are slow convergence and high computational complexity. Initiated by the merits and shortcomings of the gradient-based algorithms and the evolutionary algorithms, we developed a new hybrid search methodology in which the genetic-type search is embedded into gradient-descent algorithms (such as the LMS algorithm). The new algorithm has the characteristics of faster convergence, global search capability, less sensitivity to the choice of parameters, and simple implementation. The basic idea of the new algorithm is that the filter coefficients are evolved in a random manner once the filter is found to be stuck at a local minimum or to have a slow convergence rate. Only the fittest coefficient set survives and is adapted according to the gradient-descent algorithm until the next evolution. As the random perturbation will be subject to the stability constraint, the filter can always minimum in a stable manner and achieve a smaller error performance with a fast rate. The article reviews adaptive IIR filtering and discusses common learning algorithms for adaptive filtering. It then presents a new learning algorithm based on the genetic search approach and shows how it can help overcome the problems associated with gradient-based and GA algorithms