Some Applications of Genetic Programming in Digital Signal Processing

A significant part of Digital Signal Processing (DSP) is concerned with restoring signals corrupted by unwanted interference or noise. The usual way to recover such a signal is by adaptive filtering. Designing adaptive filters is not an easy task. It usually involves complicated algorithms whose performance depends on the skill of the designer as well as the power of the computer used. The aim of the present work is to provide a way of automating such process by means of a black box technique. In this approach, both the structure and the parameters of adaptive filters are evolved. The former is done by Genetic Programming (GP) and the latter is done by Simulated Annealing (SA). The power of the hybrid GP/SA is demonstrated with some results on three interesting DSP applications: channel equalisation, noise cancellation and interference removal.