Designing Multiplicative General Parameter Filters Using Adaptive Genetic Algorithms

Multiplicative general parameter (MGP) approach to finite impulse response (FIR) filtering introduces a novel way to realize cost effective adaptive filters in compact very large scale integrated circuit (VLSI) implementations used for example in mobile devices. MGP-filter structure comprises of additions and only a small number of multiplications, thus making the structure very simple. Only a couple of papers have been published on this recent innovation and, moreover, MGP-filters have never been designed using adaptive genetic algorithms (GA). The notion suggesting the use of adaptive parameters is that optimal parameters of an algorithm may change during the optimization process, and thus it is difficult to define parameters beforehand that would produce competitive solutions. In this paper, we present results of designing MGP-FIR basis filters using different types of adaptive genetic algorithms, and compare the results to the ones obtained using a simple GA.