In this study, the use of modified genetic algorithms (MGA) in the parameterization of the Transmission Mechanisms is facilitated. The new algorithm is proposed from the genetic algorithm with some additional strategies, and yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to compare with the LMS (Least mean-squares) method and GA method. Then, this proposed algorithm is applied to the identification of the Transmission Mechanisms of DC motor. The parameters of the friction force and DC motor are estimated in a single identification experiment. It is also shown that this technique is capable of identifying the whole transmission system. Finally, the Minimum Variance Controller (MVC) is taken to track the desired speed trajectory and then a comparison to the conventional digital PID controller is shown. Experiment results are included to demonstrate the excellent performance of the MVC.