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.
该模型有自组织和自学习的功能，可以根据每次学习误差的不同，不断调整学习速率，加速收敛过程，充分排除数据样本的随机性影响。
The network model can organize and study itself, according to different study error, continuously adjust the study rate, and accelerate refrain process, expel influence of the data sample.
用随机过程论中的马尔克夫链理论研究了几种遗传算法的收敛性。
The convergences of several genetic algorithms are studied by using Markovian chain theory in stochastic processes.
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