一个动态遗传算法代码

%IAGA

function best=ga

clear

MAX_gen=200;            %最大迭代步数

best.max_f=0;           %当前最大的适应度

STOP_f=14.5;            %停止循环的适应度

RANGE=[0 255];          %初始取值范围[0 255]

SPEEDUP_INTER=5;       %进入加速迭代的间隔

advance_k=0;            %优化的次数

popus=init;             %初始化

for gen=1:MAX_gen

    fitness=fit(popus,RANGE);       %求适应度

    f=fitness.f;

    picked=choose(popus,fitness);   %选择

    popus=intercross(popus,picked); %杂交

    popus=aberrance(popus,picked); %变异

    if max(f)>best.max_f

        advance_k=advance_k+1;

        x_better(advance_k)=fitness.x;

        best.max_f=max(f);

        best.popus=popus;

        best.x=fitness.x;

    end

    if mod(advance_k,SPEEDUP_INTER)==0

        RANGE=minmax(x_better);

        RANGE

        advance=0;

    end

end

return;

function popus=init%初始化

M=50;%种群个体数目

N=30;%编码长度

popus=round(rand(M,N));

return;

function fitness=fit(popus,RANGE)%求适应度

[M,N]=size(popus);

fitness=zeros(M,1);%适应度

f=zeros(M,1);%函数值

A=RANGE(1);B=RANGE(2);%初始取值范围[0 255]

for m=1:M

    x=0;

    for n=1:N

        x=x+popus(m,n)*(2^(n-1));

    end

    x=x*((B-A)/(2^N))+A;

    for k=1:5

        f(m,1)=f(m,1)-(k*sin((k+1)*x+k));

    end

end

f_std=(f-min(f))./(max(f)-min(f));%函数值标准化

fitness.f=f;fitness.f_std=f_std;fitness.x=x;

return;

function picked=choose(popus,fitness)%选择

f=fitness.f;f_std=fitness.f_std;

[M,N]=size(popus);

choose_N=3;                 %选择choose_N对双亲

picked=zeros(choose_N,2);   %记录选择好的双亲

p=zeros(M,1);               %选择概率

d_order=zeros(M,1);

%把父代个体按适应度从大到小排序

f_t=sort(f,'descend');%将适应度按降序排列

for k=1:M

    x=find(f==f_t(k));%降序排列的个体序号

    d_order(k)=x(1);

end

for m=1:M

    popus_t(m,:)=popus(d_order(m),:);

end

popus=popus_t;

f=f_t;

p=f_std./sum(f_std);                    %选择概率

c_p=cumsum(p)';                          %累积概率

for cn=1:choose_N

    picked(cn,1)=roulette(c_p); %轮盘赌

    picked(cn,2)=roulette(c_p); %轮盘赌

    popus=intercross(popus,picked(cn,:));%杂交

end

popus=aberrance(popus,picked);%变异

return;

function popus=intercross(popus,picked) %杂交

[M_p,N_p]=size(picked);

[M,N]=size(popus);

for cn=1:M_p

    p(1)=ceil(rand*N);%生成杂交位置

    p(2)=ceil(rand*N);

    p=sort(p);

    t=popus(picked(cn,1),p(1):p(2));

    popus(picked(cn,1),p(1):p(2))=popus(picked(cn,2),p(1):p(2));

    popus(picked(cn,2),p(1):p(2))=t;

end

return;

function popus=aberrance(popus,picked) %变异

P_a=0.05;%变异概率

[M,N]=size(popus);

[M_p,N_p]=size(picked);

U=rand(1,2);

for kp=1:M_p

    if U(2)>=P_a        %如果大于变异概率，就不变异

        continue;

    end

    if U(1)>=0.5

        a=picked(kp,1);

    else

        a=picked(kp,2);

    end

    p(1)=ceil(rand*N);%生成变异位置

    p(2)=ceil(rand*N);

    if popus(a,p(1))==1%0 1变换

        popus(a,p(1))=0;

    else

        popus(a,p(1))=1;

    end

    if popus(a,p(2))==1

        popus(a,p(2))=0;

    else

        popus(a,p(2))=1;

    end

end

return;

function picked=roulette(c_p) %轮盘赌

[M,N]=size(c_p);

M=max([M N]);

U=rand;

if U<c_p(1)

    picked=1;

    return;

end

for m=1:(M-1)

    if U>c_p(m) & U<c_p(m+1)

        picked=m+1;

        break;

    end

end