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1)  crossover probability
交叉率
1.
The Standard Genetic Algorithm(SGA) adopts constant crossover probability as well as invariable mutationprobability.
标准遗传算法采用固定的交叉率和变异率,对于求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。
2.
It also presents a new fitness function, an adaptive crossover probability and an adaptive mutation probability.
利用无符号整数数组代替传统的字符串进行二进制编码,用改进的适应度函数、自适应交叉率、自适应变异率取代传统的适应度数和固定的交叉率及变异率来改进遗传算法,并与基本遗传算法进行了实验比较,结果证明改进的遗传算法显著提高了收敛性能,并且具有很强的自适应能力。
3.
To overcome global situation problem tradition genetic algorithm has very strong robustness in finding the solution,but crossover probability and mutation probability is fixed and invariable,it caused premature convergence and running inefficient to the solution on complicated problem at later evolution process of tradition genetic algorithm.
传统遗传算法在求解全局问题具有很强的鲁棒性,但由于传统遗传算法固定的交叉率和变异率,使得传统遗传算法在求解复杂问题上存在早收敛及搜索后期运行效率低等缺点。
2)  crossover rate
交叉率
1.
The recommended range of crossover rate and mutation rate is given in simple genetic algorithm, and the two rates for selection is not correlative.
基本遗传算法给出了选取交叉率与变异率的推荐范围 ,两种概率的选取是相互独立的 。
3)  alternating power
交叉功率
1.
And in the double pumps condition,this paper describes a control tactics with an alternating power method for power-matching of engine and double pumps.
应用改进的单神经元自适应PID控制算法,构建了液压挖掘机单双泵功率匹配控制器,在双泵中,结合交叉功率控制方法,为发动机与双变量泵功率匹配提供了较好的控制策略。
4)  crossover probability
交叉概率
1.
According to the concentrating degree of fitness of the populations,a kind of adaptive crossover probability and mutation probability were designed in terms of three variables of maximal fitness,minimal fitness and average fitness of the populations,whereby the crossover probabilities and mutation probabilities of the whole populations could be ad.
为了改善传统自适应遗传算法的收敛速度以及局部收敛问题,根据种群适应度的集中程度,以种群的最大适应度、最小适应度以及适应度平均值这3个变量为基础,设计了改进的自适应交叉概率和变异概率来调整整个种群的交叉概率和变异概率,提出了一种基于种群适应度集中程度的改进自适应遗传算法。
2.
In order to avoid premature convergence and occurrence of minimal deceptive problems,which is caused by the niche technique,fuzzy control is presented for the controlling of the crossover probability P_c and muta.
遗传算法加入小生境技术后虽可保持种群群体的多样性,但是不可避免的会产生部分个体的早熟以及陷入局部最优,于是加入模糊控制思想,对种群的交叉概率 P_c 和变异概率 P_m 进行模糊控制,以此为基础,形成了一种新型的模糊控制小生境遗传算法。
3.
The adaptive crossover probability and adaptive mutation probability are designed,considering the influence of every generation to .
该算法将核引入遗传算法的初始群体来提高算法的性能,依照决策属性对条件属性的依赖度,在加强局部搜索能力的同时保持了该算法全局寻优的特性,并且对交叉概率和变异概率进行了新的设计。
5)  cross-efficiency
交叉效率
1.
Improvement of Cross-Efficiency ranking Methods;
一种用于评估排序的交叉效率方法的改进
2.
Cross-efficiency Evaluation Method Based on Superefficiency DEA Model
基于超效率DEA模型的交叉效率评价方法
3.
Identifying the high-growth technological SMEs by GEP method,a comprehensive analysis is made to those SMEs operating efficiency including technological efficiency and cross-efficiency,and the results reveal that the technological efficiency is often inconsistent with cross-efficiency.
首先通过GEP方法识别出具有高成长特征的企业,然后通过技术效率和交叉效率分析对高成长型科技中小企业的运营效率进行综合分析,发现了在技术效率和交叉效率之间存在的不一致现象。
6)  cross frequency
交叉频率
补充资料:电导率(见电阻率)


电导率(见电阻率)
conductivity

d!日nd日O}已电导率(eonduetivity)见电阻率。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条