根据编码方法设计了遗传运算的遗传算子。
The GA's operators are also designed according to the encoding method.
GP的基本遗传算子包括选择、交叉和变异。
The basic genetic operators of GP are mutation, crossover, and selection.
构造出一种基于单亲遗传算子的免疫算法用于求解此模型。
An immune algorithm based on parthenogenetic operators is proposed for solving the model.
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。
Wheel disk gambling selection operator, adaptive crossover and mutation operators are used as genetic operators.
另外，设计了一个新的加速遗传算子，可以提高实数遗传算法的收敛速度。
In addition, a new accelerating genetic operator was introduced to improve the convergence of GNN.
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 means of dynamic genetic operators and population control were used to make the evolution escape from localization trap quickly.
交叉算子是遗传算法中最主要的遗传算子，对种群的搜索性能起着重要的作用。
The crossover is a very important operator in genetic algorithms because of its ability of searching the new solution space.
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 probability character of genetic operator is also analyzed to clarify the function and interaction of genetic operators during the genetic operating process.
遗传算法应用遗传算子不断地将旧群体转化成新群体，新群体能更好地适应环境。
GA applies operators to generate new population from the old and the new one can be more adaptation to environment.
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。
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.
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。
Encode of GA and genetic operators are improved, so they are more appropriate for reliability optimization of stochastic structure.
采用这种编码方案、遗传算子和参数使得遗传操作大大简化，能达到有效的调度作用。
The use of such a coding schedule, gene and parameters make the genetic operations quite simple.
解决了基站覆盖率与经济效益之间的矛盾，并且采用实验向量的编码方式对遗传算子进行描述。
It can settle the contradiction of cell over-lay and economic benefit. And real-numbered vectors are adopted to annotate the genetic operator states.
在逆序算子和对偶算子的性能研究基础之上，设计了逆序与对偶组合遗传算子，增强了局部搜索性能。
Inverse and dual combination operator is defined as a new genetic operator based on respective application study of inverse operator and dual operator, which can improve local searching.
遗传算法中分别采用0-1编码和基于模块编号的整数编码方式，并设计了相应的适应度函数及遗传算子。
The two later methods are newly designed in the thesis. These genetic algorithms adapt 0-1 encoding and integer encoding based on module number.
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。
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.
为此引入具有强收敛性的免疫遗传算子(IGO)，测试表明结合算法IGO - PG A改进了PGA的性能。
Immune Genetic operator (IGO) is a strong convergence operator, combined with which PGA's performance can be improved.
为此引入具有强收敛性的免疫遗传算子(IGO)，测试表明结合算法IGO - PG A改进了PGA的性能。
Immune Genetic operator (IGO) is a strong convergence operator, combined with which PGA's performance can be improved.
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