code
所属分类:网络编程
开发工具:matlab
文件大小:340KB
下载次数:17
上传日期:2019-12-10 09:40:35
上 传 者:
zl97
说明: 经典链路预测的相似性算法,计算AUC,precision
(Similarity algorithm of classical link prediction, AUC, precision)
文件列表:
code (0, 2019-12-01)
code\AA.m (451, 2013-09-20)
code\ACT.m (632, 2013-09-20)
code\CN.m (218, 2019-12-09)
code\CalcAUC.m (998, 2013-09-20)
code\Copy_of_Main.m (9080, 2012-06-29)
code\Copy_of_RWR.m (1018, 2012-06-29)
code\CosPlus.m (588, 2013-09-20)
code\DivideNet.m (2269, 2013-09-21)
code\FormNet.m (618, 2013-09-21)
code\HDI.m (545, 2013-09-20)
code\HPI.m (546, 2013-09-20)
code\HistRate.m (1644, 2013-10-11)
code\Jaccard.m (700, 2013-09-20)
code\Karate Club (0, 2012-05-14)
code\Karate Club\(2003)Fast_algorithm_for_detecting_community_structure_in_networks(FN算法).pdf (181339, 2012-05-14)
code\Karate Club\GetModularity.m (1352, 2012-05-14)
code\Karate Club\dataset (0, 2012-05-14)
code\Karate Club\dataset\karate.dat (2380, 2012-05-14)
code\Karate Club\main.m (386, 2012-05-14)
code\Karate Club.rar (147824, 2013-11-04)
code\Katz.m (295, 2013-09-20)
code\LHN.m (457, 2013-09-20)
code\LHNII.m (668, 2013-09-20)
code\LNBAA.m (829, 2013-09-20)
code\LNBCN.m (810, 2013-09-20)
code\LNBRA.m (826, 2013-09-20)
code\LRW.m (758, 2013-09-20)
code\LinkPredictionMatlab (0, 2019-12-09)
code\LinkPredictionMatlab\AA.m (451, 2013-09-20)
code\LinkPredictionMatlab\ACT.m (632, 2013-09-20)
code\LinkPredictionMatlab\CN.m (206, 2013-09-20)
code\LinkPredictionMatlab\CalcAUC.m (998, 2013-09-20)
code\LinkPredictionMatlab\Copy_of_Main.m (9079, 2019-12-09)
code\LinkPredictionMatlab\Copy_of_RWR.m (1018, 2012-06-29)
code\LinkPredictionMatlab\CosPlus.m (588, 2013-09-20)
code\LinkPredictionMatlab\DivideNet.m (2269, 2019-12-01)
code\LinkPredictionMatlab\HDI.m (545, 2013-09-20)
code\LinkPredictionMatlab\HPI.m (546, 2013-09-20)
code\LinkPredictionMatlab\Jaccard.m (700, 2013-09-20)
... ...
SGA(Simple Genetic Algorithm)是一种强大的智能多变量优化算法,它模仿种群繁殖规律来进行
优化。
本SGA可以优化变量,求最小值,最大值(当把函数倒数也就求最小值啦)
并且支持浮点编码,grey编码,二进制编码;轮赌法选择,锦标赛选择;单点交叉,均布交叉,浮点交叉;
单点变异,浮点变异;
调用时
Genetic(目标函数名)
if you get some problems,you can email to me
shoppingxo@hotmail.com
qq:10901831
Xi Huabin(席华彬)
使用环境:MATLAB6.5+ToolBox
使用SGA时,
首先需要一个目标函数(像AimFunc.m),该函数返回适应度
输入变量为待优化变量x
输出为一个适应度。
然后
修改Genetic.m中可以修改的地方
例一
maxgen=200; % maximum generation
sizepop=100; % size of population
AimFunc=StrAimFunc; % this is function of counting fitness
fselect='tournament'; % method of select
% you can choose 'tournament';'roulette'
fcode='float'; % method of coding
% you can choose 'float';'grey';'binary'
pcross=[0.6]; % probablity of crossover,between 0 and 1
fcross='float'; % method of crossover
% you can choose 'float';'simple';'uniform'
pmutation=[0.2]; % probability of mutation,between 0 and 1
fmutation='float'; % method of mutation
% you can choose 'float';'simple';
lenchrom=[1 1 1 1 1]; % length of bit of every varible
bound=[0 1;...
0 1;...
0 1;...
0 1;...
0 1];
选择了浮点编码,tournament选择,浮点交叉,浮点变异。
注:采用浮点编码时,以后的交叉,变异只能是浮点,且lenchrom向量中都为1,向量长度为待优化变量个数。
bound为各个变量的范围
例二
maxgen=200; % maximum generation
sizepop=100; % size of population
AimFunc=StrAimFunc; % this is function of counting fitness
fselect='tournament'; % method of select
% you can choose 'tournament';'roulette'
fcode='binary'; % method of coding
% you can choose 'float';'grey';'binary'
pcross=[0.6]; % probablity of crossover,between 0 and 1
fcross='uniform'; % method of crossover
% you can choose 'float';'simple';'uniform'
pmutation=[0.2]; % probability of mutation,between 0 and 1
fmutation='simple'; % method of mutation
% you can choose 'float';'simple';
lenchrom=[10 10 10 10 10]; % length of bit of every varible
bound=[0 1;...
0 1;...
0 1;...
0 1;...
0 1];
选择了二进制编码,tournament选择,均布交叉,单点变异。
注:采用二进制编码和grey编码时,交叉方法只能是simple,uniform,变异方法只能是simple
lenchrom向量中表示变量表示成二进制的位数。
例三
maxgen=200; % maximum generation
sizepop=100; % size of population
AimFunc=StrAimFunc; % this is function of counting fitness
fselect='roulette'; % method of select
% you can choose 'tournament';'roulette'
fcode='grey'; % method of coding
% you can choose 'float';'grey';'binary'
pcross=[0.6]; % probablity of crossover,between 0 and 1
fcross='uniform'; % method of crossover
% you can choose 'float';'simple';'uniform'
pmutation=[0.2]; % probability of mutation,between 0 and 1
fmutation='simple'; % method of mutation
% you can choose 'float';'simple';
lenchrom=[10 10 10 10 10]; % length of bit of every varible
bound=[0 1;...
0 1;...
0 1;...
0 1;...
0 1];
选择了grey编码,roulette选择,均布交叉,单点变异。
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