A hybrid genetic algorithm for estimating the optimal time scale of linearsystems approximations using Laguerre models

In this correspondence, we deal with the problem of finding the optimal tim e scale of the truncated Laguerre series using numerical search techniques. We develop a hybrid genetic algorithm (GA) to search the nonlinear, multim odal squared-error function that results from least-squares approximations of the impulse response of causal linear time-invariant stable systems. The hybrid GA incorporates a Newton-Raphson (NR) local optimizer for fast c onvergence to the global minimum point. The proposed method competes favora bly with the pure GA in solution accuracy (the number of function evaluatio ns being the same) and with an established gradient-directed optimization a lgorithm in number of function evaluations (the solution accuracy being the same).