中文摘要：

针对基本遗传算法（简称BGA）常常存在局部收敛以及收敛解精度不高等方面的不足，提出了一种改进的算法——两阶段遗传算法，给出了算法的结构及具体的实施策略，进而利用Markov链理论和仿真技术分析了该算法的收敛性能，结果表明该算法具有操作简单、鲁棒性强等特点，不仅可以有效地避免寻优过程中的“早熟”现象，而且在很大程度上能提高最优解精度，适合于大规模、高精度的优化问题。

英文摘要：

In view of poor convergence and partially converge of basic genetic algorithm（BGA）, an new improved algorithm, two-stage genetic algorithm is proposed. The algorithm structure and its implementation strategies are demonstrated. Then, its convergence is analysed by using Markov chain theory and simulation technology. All results indicate that this new improved algorithm will help avoid ＂premature＂ phenomenon, and improve the precision of the optimal solution. It is applicable to large scale optimization problems that demand high accuracy.