动态变异遗传算法

遗传算法是根据达尔文生物进化理论而提出的一种优化算法。该文提出了一种新的遗传算法,理论分析显示,它不仅能保持遗传种群的多样性,而且能快速收敛。计算机仿真实验证明了改进后的遗传算法能够有效地克服不成熟收敛、进而搜索到全局最优解,并将这种新遗传算法用于BP网络的拓朴结构的优化和连接权值的训练,实例表明了该算法的有效性和可行性。

Dynamic Mutation Genetic Algorithm

Genetic Algorithms is Optimal Algorithm, which employ a search technique based on ideas from Darwin's natural evolution theory. A new genetic algorithm (NGA) is proposed in this paper, which not only can keep the population diversity but also has quicker convergence speed. The experiment results show that the improved genetic algorithm can efficiently find global optimal beyond premature convergence. Finally, using the NGA, optimizing the topology and training the weights for BP neural network are done. The results of the applications show that the new genetic algorithm is practical and efficient.