Simulated annealing mechanism is introduced to do local-search for the best chromosome in every generation of the evolution process. This improves the convergence of the algorithm.
算法引入模拟退火机制，在遗传进化过程中的每一代，对最优个体进行邻域局部寻优，利用模拟退火进一步改善算法的收敛性能。
By contrasting the traditional genetic algorithms (TGA) with the biologic evolution, two kinds of reasons that the convergence speed and searching efficiency in TGA are both lower are concluded.
比照传统遗传算法与生物界进化过程，分析了引起传统遗传算法收敛速度慢和寻优效率低的两个原因。
Network evolution and convergence must be done on the basis of current network, we can not re-construct a brand-new network despite of what we have now.
网络的演进和融合必须基于原有网络，不能抛开已投建的网络重新建立一套全新的系统。
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