In addition , we make research on the convergence of genetic algorithms by analyzing the globality and monotonicity of the schemata . it is demonstrated that for some kinds of the problems , the method of searching global best schemata can greatly decrease the ix abstract solution space and lead to converging quickly and successfully to the optimum . the monotonicity of schemata is used to determine deceptive problems and , with the aid of absorbing schemata , to analyze the schematic factors leading to ga - hard problems 另外，通过分析模式的全局性和单调性，证明对于某些问题搜索其全局最优模式可以大大缩小解空间，从而导致更快、更顺利地收敛到最优解，并把模式的单调性用于对遗传算法欺骗问题的划分，结合吸收模式的性质，进一步探讨影响遗传算法困难的模式因素。
The application for some practice instances in some domains is introduced . the satisfactory degree function based on the objective function , by the cost of the solution search , by the proportion of the search process in the solution space , using the fuzzy logic and based on the neural network are put forward 并就几个应用领域，针对一些具体情况进行了分析，介绍了满意度原理在这些领域的应用方向；提出了基于目标函数的、按照解的搜索代价的、依据解空间搜索比例的、利用模糊逻辑的以及基于神经网络的满意度函数的建立方法。
Here scheme design evolutes in system optimizer ; detail design consists of various parts , and each part evolutes in different sub - optimizer . distributed co - evolution introduces " fittest survival " law of genetic algorithm , and adds hierarchical co - evolution of problem space and solution space 分布式协同演化算法，引入了遗传算法的优胜劣汰法则，加入了问题空间与解空间的层次性协同演化，为变化的方案提供变化的细部探索区域，适用于诸如基坑支护优化问题等参数离散的、带层次性的复杂工程问题。
