Genetic algorithm processor for adaptive IIR filters

It is well known that gradient search fails in adaptive IIR filters because their error surfaces may be multi-modal. In this paper, a genetic algorithm approach is presented to overcome the problem. Instead of applying conventional deterministic methods to optimise the filter coefficients, genetic algorithms are used. They have a well established mathematical foundation and global optimisation capability. We started with a processor model of a genetic algorithm called GAP. We compared the performance of the GAP with conventional and other stochastic algorithms. This example shows the fast convergence and adaptive behaviour of the GAP system