Evolutionary Design of Digital Filter Using Multiple Pareto Fronts

Traditionally digital filters are designed using the concept of a linear difference equation with the output response being a weighted sum of signal samples with usually floating point coefficients. Unfortunately such a model is necessarily expensive in terms of hardware as it requires many large bit additions and multiplications. In this paper, An alternative approaches to the design of digital filter has been proposed using Multi-objective evolutionary algorithms and the concept of Evolvable Hardware. In the evolution process, clustering and Pareto optimization are combined into a single evolutionary design algorithm to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfill the design criteria. This approach is demonstrated using a polar coordinate based pole-zero representation. The results demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process. Pareto optimization evolution design had been proved to be a stable design method.