遗传算法的优化计算.doc

遗传算法的优化计算
function Val=de_code(x)
% 全局变量声明
global S P_train T_train P_test T_test mint maxt
global p t r s s1 s2
% 数据提取
x=x(:,1:S);
[m,n]=find(x==1);
p_train=zeros(size(n,2),size(T_train,2));
p_test=zeros(size(n,2),size(T_test,2));
for i=1:length(n)
p_train(i,:)=P_train(n(i),:);
p_test(i,:)=P_test(n(i),:);
end
t_train=T_train;
p=p_train;
t=t_train;
% 遗传算法优化BP网络权值和阈值
r=size(p,1);
s2=size(t,1);
s=r*s1+s1*s2+s1+s2;
aa=ones(s,1)*[-1,1];
popu=20; % 种群规模
initPpp=initializega(popu,aa,'gabpEval'); % 初始化种群
gen=100; % 遗传代数
% 调用GAOT工具箱,其中目标函数定义为gabpEval
x=ga(aa,'gabpEval',[],initPpp,[1e-6 1 0],'maxGenTerm',gen,...
'normGeomSelect',,'arithXover',2,'nonUnifMutation',[2 gen 3]); % 创建BP网络
net=newff(minmax(p_train),[s1,1],{'tansig','purelin'},'trainlm'); % 将优化得到的权值和阈值赋值给BP网络
[W1,B1,W2,B2]=gadecod(x);
{1,1}=W1;
{2,1}=W2;
{1}=B1;
{2}=B2;
% 设置训练参数
=1000;
=10;
=;
=;
=0;
% 训练网络
net=,p_train,t_train);
% 仿真测试
tn_sim=,p_test);
% 反归一化
t_sim=postmnmx(tn_sim,mint,maxt);
% 计算均方误差
SE=sse(t_sim-T_test);
% 计算适应度函数值
Val=1/SE;
end
function [sol,Val]=fitness(sol,options)
global S
for i=1:S
x(i)=sol(i);
end
Val=de_code(x);
end
%
%% 清空环境变量
clear all
clc
warning off
%% 声明全局变量
global P_train T_train P_test T_test mint maxt S s1 S=30;
s1=50;
%% 导入数据
load
a=randperm(569);
Train=data(a(1:500),:);
Test=data(a(501:end),:);
% 训练数据
P_train=Train(:,3:end)';
T_train=Train(:,2)';
% 测试数据
P_test=Test(:,3:end)';
T_test=Test(:,2)';
% 显示实验条件
total_B=length(find(data(:,2)==1));
total_M=length(find(data(:,2)==2));
count_B=length(find(T_train==1));
count_M=length(find(T_train==2));
number_B=length(find(T_test==1));
number_M=length(f