Optimal design of FRM-Based FIR filters by using hybrid Taguchi genetic algorithm

This paper presents a hybrid Taguchi genetic algorithm (HTGA) to optimize all coefficient values of the FRM filters simultaneously. The HTGA approach is a method combining the traditional genetic algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is an experimental design method, which is inserted between crossover and mutation operations of a GA to enhance the genetic algorithm so that better potential offspring can be generated. The resulting algorithm can be more robust and statistically sound. Therefore, the proposed HTGA approach has the advantages of global exploration, fast convergence, and robustness. Experimental results show that this approach has good performance on optimizing the coefficient values of a FRM filter.