对经典的遗传算法在计算中出现的随机性问题，则采用压缩映射遗传算法使计算过程渐近收敛。
For the randomness of classical GA which appears in calculation process, the contraction mapping GA is applied to make the calculation asymptotically convergent.
遗传算法和混沌遗传算法的收敛速度比经典的线性优化方法慢很多，但改进后其速度还是可以接受的，且混沌遗传算法的收敛速度比同等条件下的遗传算法速度快得多。
Both the convergence speeds of CGA and GA are much slower than the classic linear methods, but their speeds after improved are acceptable, and the convergence speed of CGA is faster than that of GA.
与经典优化算法不同的特点在于，遗传算法是从多个初始点开始寻优，沿多路径搜索实现全局或准全局最优。
The GA uses a population of points at a time in contrast to the single point approach by the traditional ones, and will guarantee to find the global optimum.
与经典优化算法不同的特点在于，遗传算法是从多个初始点开始寻优，沿多路径搜索实现全局或准全局最优。
The GA uses a population of points at a time in contrast to the single point approach by the traditional ones, and will guarantee to find the global optimum.
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