The span analysis of the genetic operators.
遗传操作算子取值分析。
The basic genetic operators of GP are mutation, crossover, and selection.
GP的基本遗传算子包括选择、交叉和变异。
These genetic operators play an important role in experimental verification.
通过实验验证了遗传算子对算法的优化性能。
Wheel disk gambling selection operator, adaptive crossover and mutation operators are used as genetic operators.
其中遗传算子分别采用轮盘赌选择算子以及自适应的交叉和变异算子。
Encode of GA and genetic operators are improved, so they are more appropriate for reliability optimization of stochastic structure.
对遗传算法的编码、遗传算子进行了改进，使其更加适用于随机结构的可靠性优化。
The means of dynamic genetic operators and population control were used to make the evolution escape from localization trap quickly.
采用动态遗传算子设计和群体规模控制方法，使进化更快速跳出局部最优。
The probability character of genetic operator is also analyzed to clarify the function and interaction of genetic operators during the genetic operating process.
遗传操作的概率特征，揭示了遗传算子各自在遗传优化过程中的作用及相互关系。
The new GA takes the advantages of the global searching of genetic operators based on probabilities and the advantages of the local searching of the new operators.
该算法充分利用基于概率的遗传算子的全局搜索能力和新算子较强的局部搜索能力。
Results From the description of the solving to the genetic operators, PSO operators and the construction of GA-PSO frame, a integrated GA-PSO programming is presented.
结果从解的描述、遗传算子、PS O运算符的构造再到GA - P SO算法框架，提出了完整的GA - P SO混合规划算法。
The selection of the key parameters and genetic operators is discussed. The calculation results of three computational examples are given. The characteristic of the three methods is pointed.
通过对连续变量无约束优化、连续变量约束优化和离散变量约束优化等典型优化问题的计算分析 ，将三种惩罚函数方法进行了比较 ，指出了它们的特点及选用原则。
And the combination of an instance of the knapsack problem, given specific encoding method, operating parameters, population size, maximum number of iterations, and appropriate genetic operators.
并且结合背包问题实例，给出了具体的编码方法，运行参数，群体大小，最大迭代次数，以及合适的遗传算子。
By choosing appropriate operators and parameters, genetic algorithms (GA) can solve the traveling salesman problem (TSP) effectively.
通过选择合适的算子和参数，遗传算法(GA)可以有效求解旅行商问题(tsp)。
By choosing appropriate operators and parameters, genetic algorithms (GA) can solve the traveling salesman problem (TSP) effectively.
通过选择合适的算子和参数，遗传算法(GA)可以有效求解旅行商问题(tsp)。
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