Design of discrete coefficient FIR filters by simulated evolution

The article presents a new algorithm, simulated evolutionary optimization, a multi-agent stochastic search method for optimizing the coefficients of a FIR digital filter in the powers-of-two space by minimizing a sum of two cost functions, namely the squared error between discrete filter frequency response and desired frequency response and the squared error between discrete and infinite precision coefficients. The proposed algorithm requires less computation time than existing techniques based on linear programming and simulated annealing and differs from the tree search based algorithms in optimizing the coefficients simultaneously. The effectiveness of the method is demonstrated through a low-pass filter example.