利用主成分分析法获取端到端网络流量的主要特征分量并获得其初始估计结果.为克服其初值敏感性将估计结果作为遗传算法的初始值、链路流量估计偏差作为遗传算法的适应度函数, 通过构建合适的交叉和变异概率函数来控制遗传算法的交叉和变异过程.采用合适的约束迭代函数,利用遗传算法通过迭代寻优获得端到端流量的估计结果， 仿真结果表明所提出的方法是可行的.

Principal component analysis was exploited to extract the principal features of endtoend network traffic and to attain the initial estiamtion results. This results are taken as the prior value of genetic algorithm to overcome its sensitiveness to the prior value. The estiamtion biases of link traffic is regarded as the fitness function of genetic algorithm. The crossover and mutation probability functions are built to control its corossover and mutation processes. The appropriate iterative funtion with contraints is built. The genetic algorithm is used to attain the endtoend traffic estimation results in the iterative way. Simulation results show that the proposed method is feasible.