赠MATLAB遗传算法代码

ID:526755 · 发表于 2019-5-4 02:45

赠MATLAB代码与有缘人

遗传算法
function error = fun(x,inputnum,hiddennum,hiddennum,outputnum,net,inputn,outputn)
x1 = x(1:inputnum*hiddennum);
B1 = x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum);
w2 = x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum);
B2 = x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum);
net.iw{1,1}=reshape(w1,hiddennum,inputnum);
net.lw{2,1}=reshape(w2,outputnum,hiddennum);
net.b{1}=reshape(B1,hiddennum,1);
net.b{2}=B2;
net=newff(inputn,outputn,hiddennum);
net.trainParam.epochs=20;
net.trainParam.lr=0.1;
net.trainParam.goal=0.00001;
net.trainParam.show=100;
net.trainParam.showWindow=0;
net=train(net,inputn,outputn);
an=sim(net,inputn);
error=sum(abs(an-outputn));
选择操作
function ret=select(individuals,sizepop)
fitness1=10./individuals.fitness;
sumfitness=sum(fitness1);
sumf=fitmessl./sumfitness;
index=[];
for i=1:sizepop
pick=rand;
while pick==0
pick=rand;
end
for i=1:sizepop
pick=pick-sumf(i);
if pick<0
index=[index i];
break;
end
end
end
individuals.chrom=individuals.chrom(index,:);
individuals.fitness=individuals.fitness(index);
ret=individuals;
交叉操作
function ret=Cross(pcross,lenchrom,chrom,sizepop,bound)
for i=1:sizepop
pick=rand(1,2);
while prod(pick)==0
pick=rand(1,2);
end
index=ceil(pick.*sizepop);
pick=rand;
while pick==0
pick=rand;
end
if pick>pcross
continue;
end
flag=0;
while flag==0
pick=rand;
end
pos=ceil(pick.*sum(lenchrom));
pick=rand;
v1=chrom(index(1),pos);
v2=chrom(index(2),pos);
chrom(index(1),pos)=pick*v2+(1-pick)*v1;
chrom(index(2),pos)=pick*v1+(1-pick)*v2;
flag1=test(lenchrom,bound,chrom(index(1),:));
flag2=test(lenchrom,bound,chrom(index(2),:));
if flag1*flag2==0
flag=0;
else flag=1;
end
end
end
ret=chrom;
变异操作
function ret=Mutation(pmutation,lenchrom,chrom,sizepop,num,maxgen,bound)
for i=1:sizepop
pick=rand;
while pick==0
pick=rand;
end
index=ceil(pick*sizepop);
pick=rand;
if pick>pmutation
continue;
end
flag=0;
while flag==0
pick=rand;
while pick==0
pick=rand;
end
pos=ceil(pick*sum(lenchrom));
v=chrom(i,pos);
v1=v-bound(pos,1);
v2=bound(pos,2)-v;
pick=rand;
fg=(rand*(1-num/maxgen))^2;
if pick>0.5
chrom(i,pos)=chrom(i,pos)+(bound(pos,2)-chrom(i,pos))*fg;
else
chrom(i,pos)=chrom(i,pos)-(bound(i,pos)-bound(pos,1))*fg;
end
flag=test(lenchrom,boun,chrom(i,;));
end
end
ret=chrom;
遗传算法主函数
clc
clear
load data input output
inputnum=2;
hiddennum=5;
outputnum=1;
input_train=input(1:1900,:)';
input_test=input(1901:2000,:)';
output_train=output(1:1900)';
output_test=output(1901:2000)';
[inputn,inputps]=mapminmax(input_train);
[outputn,outputps]=mapminmax(output_train);
net=newff(inputn,outputn,hiddennum);
maxgen=50;
sizepop=10;
pcross=[0.4];
pmutation=[0.2];
numsum=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum;
lenchrom=ones(1,numsum);
bound=[-3*ones(numsum,1)3*ones(numsum,1)];
individuals=struct('fitness',zeros(1,sizepop),'chrom',[]);
avgfitness=[];
bestfitness=[];
bestchrom=[];
for i=1:sizepop
individuals.chrom(i,:)=Code(lenchrom,bound);
x=individuals.chrom(i,:);
individuals.fitness(i)=fun(x,inputnum,hiddennum,outputnum,net,inputn,outputn);
end
for i=1:maxgen
individuals=Select(individuals,sizepop);
individuals.chrom=Cross(pcross,lenchrom,individuals.chrom,sizepop,i,maxgen,bound);
for j=1:sizepop
x=individuals.chrom(j,:);
individuals.fitness(j)=fun(x,inputnum,hiddennum,outputnum,net,inputn,output n);
end
[newbestfitness,newbestindex]=min(individuals.fitness);
[worestfitness,worestindex]=max(individuals.fitness);
if bestfitness>newbestfitness
bestfitness=newbestfitness;
bestchrom=individuals.chrom(newbestindex,:);
end
individuals.chrom(worestindex,:)=bestchrom;
individuals.fitness(worestindex,:)=bestfitness;
avgfitness=sum(individuals.fitness)/sizepop;
trace=[trace;avgfitness bestfitness];
end
遗传算法优化的BP神经网络函数拟合
x=bestchrom
x1=x(1:inputnum*hiddennum);
B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum);
w2=x(inputnum*hiddennum+hiddennum+1;inputnum*hiddennum+hiddennum+hiddennum*outputnum);
B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1;inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum);
net.iw{1,1}=reshape(w1,hiddennum,inputnum);
net.lw{2,1}=reshape(w2,outputnum,hiddennum);
net.b{1}=reshape(B1,hiddennum,1);
net.b(2)=B2;
net.trainParam.epochs=100;
net.trainParam.lr=0.1;
[net,per2]=trian(net,inputn,outputn);
inputn_test=mapminmax('apply',input_test,inputps);
an=sim(net,inputn_test);
test_simu=mapminmax('reverse',an,outputps);

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