www.pudn.com > PSO_GA_RBF.rar > GA.m, change:2009-05-01,size:2687b

```clear all
close all

%遗传算法优化来训练RBF网络权值
%G为进化代数,Size为种群规模,CodeL为参数的二进制编码长度
G = 250;
Size = 30;
CodeL = 10;

%确定每个参数的最大最小值
for i = 1:3
MinX(i) = 0.1*ones(1);
MaxX(i) = 3*ones(1);
end
for i = 4:1:9
MinX(i) = -3*ones(1);
MaxX(i) = 3*ones(1);
end
for i = 10:1:12
MinX(i) = -ones(1);
MaxX(i) = ones(1);
end

%初始化种群
E = round(rand(Size,12*CodeL));

BsJ = 0;

%进化开始
for kg = 1:1:G
time(kg) = kg
for s = 1:1:Size
m = E(s,:);    %取出其中个体

%把二进制表示的参数转化为实数
for j = 1:1:12
y(j) = 0;
mj = m((j-1)*CodeL + 1:1:j*CodeL);
for i = 1:1:CodeL
y(j) = y(j) + mj(i)*2^(i - 1);
end
f(s,j) = (MaxX(j) - MinX(j))*y(j)/1023 + MinX(j);
end
p = f(s,:);
[p,BsJ] = fitness(p,BsJ);
BsJi(s) = BsJ;             %记录每个个体的总误差
end

%对误差排序，求出最好误差
[OderJi,IndexJi] = sort(BsJi);
BestJ(kg) = OderJi(1);
BJ = BestJ(kg);
Ji = BsJi + 1e-10;

%对误差取倒数，求出适应度值
fi = 1./Ji;    %适应度值
[Oderfi,Indexfi] = sort(fi);
Bestfi = Oderfi(Size);      %最佳适应度值
BestS = E(Indexfi(Size),:);     %最佳个体

kg  %进化次数
p    %最佳个体
BJ   %最佳个体的误差

%**************Step 2:选择操作**********************%
fi_sum = sum(fi);
fi_Size = (Oderfi/fi_sum)*Size;

fi_S = floor(fi_Size);

kk = 1;
for i = 1:1:Size
for j = 1:1:fi_S(i)
TempE(kk,:) = E(Indexfi(i),:);
kk = kk + 1;
end
end

%***************Step 3:交叉操作***********************************%
pc = 0.60;
n = ceil(20*rand);
for i = 1:2:(Size-1)
temp = rand;
if pc>temp
for j = n:1:20
TempE(i,j) = E(i+1,j);
TempE(i+1,j) = E(i,j);
end
end
end
TempE(Size,:) = BestS;
E = TempE;

%***************Step 4:变异操作**********************************%
pm = 0.001 - [1:1:Size]*(0.001)/Size;
for i = 1:1:Size
for j = 1:1:12*CodeL
temp = rand;
if pm>temp
if TempE(i,j) == 0
TempE(i,j) = 1;
else
TempE(i,j) = 0;
end
end
end
end

%把最佳个体赋于种群中
TempE(Size,:) = BestS;
E = TempE;

end

Bestfi
BestS
fi
Best_J = BestJ(G)
figure(1)
plot(time,BestJ);
title('遗传算法优化RBF网络权值中最小误差进化过程')
xlabel('进化次数');
ylabel('最小误差');
save pfile p;```