An enhanced GA technique for system optimization

The commonly used genetic algorithms (GAs) have some shortcomings in applications such as lengthy computations and slow convergence. A novel enhanced genetic algorithm, EGA, technique is developed in this paper to overcome these problems to enhance the efficiency in system training and optimization. The proposed EGA technique involves two approaches: a) a novel group-based branch crossover operator is suggested to thoroughly explore local space and to speed convergence, and b) an enhanced MPT (Makinen-Periaux-Toivanen) mutation operator is proposed to promote global search capability. The effectiveness of the developed EGA is verified by simulations using benchmark test problems. Test results show that the EGA technique can improve the classical GA methods with respect to convergence speed and global search capability.