Improving Optimization Speed for Genetic Algorithms
遗传算法优化速度的改进

The disadvantage of the traditional mutation operator of GAs was analyzed in this paper, and a DMO (dyadic mutation operator) was presented to take the place of the traditional one. The function of DMO to prevent premature convergence was also discussed. Meanwhile, according to the features of binary-based GAs, an implicit implementation for decoding the chromosomes for GAs was presented so that the run time of the improved program for GAs was shortened by 6～50 times compared with the original one. The performance of the genetic algorithm is tested based on the DMO (GADMO) in several aspects. The experimental results show that the GADMO can converge quickly and its robustness of parameters is strong. The GADMO can prevent the premature convergence effectively. By improving the mutation operator and the decoding algorithm, the optimization speed of GA is speeded up greatly.