- kaiguandianyuan 陶显芳老师开关电源的原理及其设计
- SeekerBarSample Java源码
- Segmentation-combining-of-GA-and-ANN 结合遗传算法与神经网络的图像分割
- AT89C52PTLC2543 protues仿真的AT89C52+TLC2543原理图以及例程
- RBF-neural-network 用四元十字阵做被动声定位算法设计
- Work 合并csv及类似文件 调用格式: python "merge file by line.py" target.csv D:/source/pattern*.csv target.csv 输出结果路径 D:/source/pattern*.csv 输入文件路径
文件名称:yichuansuanfaC
介绍说明－－下载内容来自于网络，使用问题请自行百度
遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型.
生存+检测的迭代搜索过程是它的核心.
具体分成五部,其中每步就是程序实现过程:
参数编码(实际问题编码到遗传基因),初始群体设定(祖先),适应度函数的设计(生存选择),遗传操作设计(遗传+变异),控制参数设计(交叉率0.2-0.99,变异率0.001-0.1).
-Genetic algorithms are simulated Darwinian natural selection of genetic selection and biological evolution of the computational model. Survival+ testing iterative search process is its core. Concrete into five, each of which is the process step implementation process: parameter code (the actual problem encoded genes), the initial group setting (ancestors), the design of fitness function (survival selection), design of genetic manipulation (genetic+ variation), the control design parameters (crossover rate of 0.2-0.99, mutation rate 0.001-0.1) .
生存+检测的迭代搜索过程是它的核心.
具体分成五部,其中每步就是程序实现过程:
参数编码(实际问题编码到遗传基因),初始群体设定(祖先),适应度函数的设计(生存选择),遗传操作设计(遗传+变异),控制参数设计(交叉率0.2-0.99,变异率0.001-0.1).
-Genetic algorithms are simulated Darwinian natural selection of genetic selection and biological evolution of the computational model. Survival+ testing iterative search process is its core. Concrete into five, each of which is the process step implementation process: parameter code (the actual problem encoded genes), the initial group setting (ancestors), the design of fitness function (survival selection), design of genetic manipulation (genetic+ variation), the control design parameters (crossover rate of 0.2-0.99, mutation rate 0.001-0.1) .
(系统自动生成,下载前可以参看下载内容)
下载文件列表
遗传算法C.doc
本网站为编程资源及源代码搜集、介绍的搜索网站，版权归原作者所有！ 粤ICP备11031372号
1999-2046 搜珍网 All Rights Reserved.