三个遗传算法matlab程序实例

遗传算法程序（一）:

   说明: fga.m 为遗传算法的主程序; 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!

function [BestPop,Trace]=fga(FUN,LB,UB,eranum,popsize,pCross,pMutation,pInversion,options) % [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation) % Finds a maximum of a function of several variables. % fmaxga solves problems of the form: %      max F(X) subject to: LB <= X <= UB                            % BestPop       - 最优的群体即为最优的染色体群 % Trace         - 最佳染色体所对应的目标函数值 % FUN           - 目标函数 % LB            - 自变量下限 % UB            - 自变量上限 % eranum        - 种群的代数,取100--1000(默认200) % popsize       - 每一代种群的规模；此可取50--200(默认100) % pcross        - 交叉概率,一般取0.5--0.85之间较好(默认0.8) % pmutation     - 初始变异概率,一般取0.05-0.2之间较好(默认0.1) % pInversion    - 倒位概率,一般取0.05－0.3之间较好(默认0.2) % options       - 1*2矩阵,options(1)=0二进制编码(默认0),option(1)~=0十进制编 %码,option(2)设定求解精度(默认1e-4) % % ------------------------------------------------------------------------ T1=clock; if nargin<3, error('FMAXGA requires at least three input arguments'); end if nargin==3, eranum=200;popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end if nargin==4, popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end if nargin==5, pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end if nargin==6, pMutation=0.1;pInversion=0.15;options=[0 1e-4];end if nargin==7, pInversion=0.15;options=[0 1e-4];end if find((LB-UB)>0)    error('数据输入错误,请重新输入(LB 
  
    1时,b(i)=mod(a(i-1)+a(i),2)     %其中原二进制串:a(1)a(2)...a(n),Gray串:b(1)b(2)...b(n)     initpop(i,:)=pop(1:end-1); end initpop(popsize,:)=ones(1,len);%The whole one encoding individual %解码 function [fval] = b2f(bval,bounds,bits) % fval   - 表征各变量的十进制数 % bval   - 表征各变量的二进制编码串 % bounds - 各变量的取值范围 % bits   - 各变量的二进制编码长度 scale=(bounds(:,2)-bounds(:,1))'./(2.^bits-1); %The range of the variables numV=size(bounds,1); cs=[0 cumsum(bits)]; for i=1:numV a=bval((cs(i)+1):cs(i+1)); fval(i)=sum(2.^(size(a,2)-1:-1:0).*a)*scale(i)+bounds(i,1); end %选择操作 %采用基于轮盘赌法的非线性排名选择 %各个体成员按适应值从大到小分配选择概率： %P(i)=(q/1-(1-q)^n)*(1-q)^i, 其中 P(0)>P(1)>...>P(n), sum(P(i))=1 function [selectpop]=NonlinearRankSelect(FUN,pop,bounds,bits) global m n selectpop=zeros(m,n); fit=zeros(m,1); for i=1:m     fit(i)=feval(FUN(1,:),(b2f(pop(i,:),bounds,bits)));%以函数值为适应值做排名依据 end selectprob=fit/sum(fit);%计算各个体相对适应度(0,1) q=max(selectprob);%选择最优的概率 x=zeros(m,2); x(:,1)=[m:-1:1]'; [y x(:,2)]=sort(selectprob); r=q/(1-(1-q)^m);%标准分布基值 newfit(x(:,2))=r*(1-q).^(x(:,1)-1);%生成选择概率 newfit=cumsum(newfit);%计算各选择概率之和 rNums=sort(rand(m,1)); fitIn=1;newIn=1; while newIn<=m     if rNums(newIn) 
   
     =pCross); len=length(y1); if len>2&mod(len,2)==1%如果用来进行交叉的染色体的条数为奇数，将其调整为偶数     y2(length(y2)+1)=y1(len);     y1(len)=[]; end if length(y1)>=2    for i=0:2:length(y1)-2        if opts==0            [NewPop(y1(i+1),:),NewPop(y1(i+2),:)]=EqualCrossOver(OldPop(y1(i+1),:),OldPop(y1(i+2),:));        else            [NewPop(y1(i+1),:),NewPop(y1(i+2),:)]=MultiPointCross(OldPop(y1(i+1),:),OldPop(y1(i+2),:));        end    end     end NewPop(y2,:)=OldPop(y2,:); %采用均匀交叉 function [children1,children2]=EqualCrossOver(parent1,parent2) global n children1 children2 hidecode=round(rand(1,n));%随机生成掩码 crossposition=find(hidecode==1); holdposition=find(hidecode==0); children1(crossposition)=parent1(crossposition);%掩码为1，父1为子1提供基因 children1(holdposition)=parent2(holdposition);%掩码为0，父2为子1提供基因 children2(crossposition)=parent2(crossposition);%掩码为1，父2为子2提供基因 children2(holdposition)=parent1(holdposition);%掩码为0，父1为子2提供基因 %采用多点交叉，交叉点数由变量数决定 function [Children1,Children2]=MultiPointCross(Parent1,Parent2) global n Children1 Children2 VarNum Children1=Parent1; Children2=Parent2; Points=sort(unidrnd(n,1,2*VarNum)); for i=1:VarNum     Children1(Points(2*i-1):Points(2*i))=Parent2(Points(2*i-1):Points(2*i));     Children2(Points(2*i-1):Points(2*i))=Parent1(Points(2*i-1):Points(2*i)); end %变异操作 function [NewPop]=Mutation(OldPop,pMutation,VarNum) global m n NewPop r=rand(1,m); position=find(r<=pMutation); len=length(position); if len>=1    for i=1:len        k=unidrnd(n,1,VarNum); %设置变异点数，一般设置1点        for j=1:length(k)            if OldPop(position(i),k(j))==1               OldPop(position(i),k(j))=0;            else               OldPop(position(i),k(j))=1;            end        end    end end NewPop=OldPop; %倒位操作 function [NewPop]=Inversion(OldPop,pInversion) global m n NewPop NewPop=OldPop; r=rand(1,m); PopIn=find(r<=pInversion); len=length(PopIn); if len>=1     for i=1:len         d=sort(unidrnd(n,1,2));         if d(1)~=1&d(2)~=n            NewPop(PopIn(i),1:d(1)-1)=OldPop(PopIn(i),1:d(1)-1);            NewPop(PopIn(i),d(1):d(2))=OldPop(PopIn(i),d(2):-1:d(1));            NewPop(PopIn(i),d(2)+1:n)=OldPop(PopIn(i),d(2)+1:n);        end    end end 
    
  

遗传算法程序（二）:

function youhuafun D=code; N=50;         % Tunable maxgen=50;     % Tunable crossrate=0.5; %Tunable muterate=0.08; %Tunable generation=1;   num = length(D); fatherrand=randint(num,N,3); score = zeros(maxgen,N); while generation<=maxgen    ind=randperm(N-2)+2; % 随机配对交叉    A=fatherrand(:,ind(1:(N-2)/2));    B=fatherrand(:,ind((N-2)/2+1:end)); %     多点交叉    rnd=rand(num,(N-2)/2);    ind=rnd   tmp=A(ind);    A(ind)=B(ind);    B(ind)=tmp; % % 两点交叉 %     for kk=1:(N-2)/2 %         rndtmp=randint(1,1,num)+1; %         tmp=A(1:rndtmp,kk); %         A(1:rndtmp,kk)=B(1:rndtmp,kk); %         B(1:rndtmp,kk)=tmp; %     end    fatherrand=[fatherrand(:,1:2),A,B];        % 变异    rnd=rand(num,N);    ind=rnd   [m,n]=size(ind);    tmp=randint(m,n,2)+1;    tmp(:,1:2)=0;    fatherrand=tmp+fatherrand;    fatherrand=mod(fatherrand,3); %     fatherrand(ind)=tmp;        %评价、选择    scoreN=scorefun(fatherrand,D);% 求得N个个体的评价函数    score(generation,:)=scoreN;    [scoreSort,scoreind]=sort(scoreN);    sumscore=cumsum(scoreSort);    sumscore=sumscore./sumscore(end);    childind(1:2)=scoreind(end-1:end);    for k=3:N        tmprnd=rand;        tmpind=tmprnd       difind=[0,diff(tmpind)];        if ~any(difind)            difind(1)=1;        end        childind(k)=scoreind(logical(difind));    end    fatherrand=fatherrand(:,childind);        generation=generation+1; end % score maxV=max(score,[],2); minV=11*300-maxV; plot(minV,'*');title('各代的目标函数值'); F4=D(:,4); FF4=F4-fatherrand(:,1); FF4=max(FF4,1); D(:,5)=FF4; save DData D function D=code load youhua.mat % properties F2 and F3 F1=A(:,1); F2=A(:,2); F3=A(:,3); if (max(F2)>1450)||(min(F2)<=900)    error('DATA property F2 exceed it''s range (900,1450]') end % get group property F1 of data, according to F2 value F4=zeros(size(F1)); for ite=11:-1:1    index=find(F2<=900+ite*50);    F4(index)=ite; end D=[F1,F2,F3,F4]; function ScoreN=scorefun(fatherrand,D) F3=D(:,3); F4=D(:,4); N=size(fatherrand,2); FF4=F4*ones(1,N); FF4rnd=FF4-fatherrand; FF4rnd=max(FF4rnd,1); ScoreN=ones(1,N)*300*11; % 这里有待优化 for k=1:N    FF4k=FF4rnd(:,k);    for ite=1:11        F0index=find(FF4k==ite);        if ~isempty(F0index)            tmpMat=F3(F0index);            tmpSco=sum(tmpMat);            ScoreBin(ite)=mod(tmpSco,300);        end    end    Scorek(k)=sum(ScoreBin); end ScoreN=ScoreN-Scorek; 

遗传算法程序（三）：

%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(m) & U 
    
  

全方位的两点杂交、两点变异的改进的加速遗传算法（IAGA）