GA.rar_MATLAB程序_遗传算法

function main() clear; clc; %种群大小 popsize=100; %二进制编码长度 chromlength=10; %交叉概率 pc = 0.6; %变异概率 pm = 0.001; %初始种群 pop = initpop(popsize,chromlength); for i = 1:100 %计算适应度值（函数值） objvalue = cal_objvalue(pop); fitvalue = objvalue; %选择操作 newpop = selection(pop,fitvalue); %交叉操作 newpop = crossover(newpop,pc); %变异操作 newpop = mutation(newpop,pm); %更新种群 pop = newpop; %寻找最优解 [bestindividual,bestfit] = best(pop,fitvalue); x2 = binary2decimal(bestindividual); x1 = binary2decimal(newpop); y1 = cal_objvalue(newpop); if mod(i,10) == 0 figure; fplot('10*sin(5*x)+7*abs(x-5)+10',[0 10]); hold on; plot(x1,y1,'*'); title(['迭代次数为n=' num2str(i)]); %plot(x1,y1,'*'); end end fprintf('The best X is --->>%5.2f\n',x2); fprintf('The best Y is --->>%5.2f\n',bestfit); %初始化种群大小 %输入变量： %popsize：种群大小 %chromlength：染色体长度-->>转化的二进制长度 %输出变量： %pop：种群 function pop=initpop(popsize,chromlength) pop = round(rand(popsize,chromlength)); %rand(3,4)生成3行4列的0-1之间的随机数 % rand(3,4) % % ans = % % 0.8147 0.9134 0.2785 0.9649 % 0.9058 0.6324 0.5469 0.1576 % 0.1270 0.0975 0.9575 0.9706 %round就是四舍五入 % round(rand(3,4))= % 1 1 0 1 % 1 1 1 0 % 0 0 1 1 %所以返回的种群就是每行是一个个体，列数是染色体长度 %二进制转化成十进制函数 %输入变量： %二进制种群 %输出变量 %十进制数值 function pop2 = binary2decimal(pop) [px,py]=size(pop); for i = 1:py pop1(:,i) = 2.^(py-i).*pop(:,i); end %sum(.,2)对行求和，得到列向量 temp = sum(pop1,2); pop2 = temp*10/1023; %计算函数目标值 %输入变量：二进制数值 %输出变量：目标函数值 function [objvalue] = cal_objvalue(pop) x = binary2decimal(pop); %转化二进制数为x变量的变化域范围的数值 objvalue=10*sin(5*x)+7*abs(x-5)+10; %如何选择新的个体 %输入变量：pop二进制种群，fitvalue：适应度值 %输出变量：newpop选择以后的二进制种群 function [newpop] = selection(pop,fitvalue) %构造轮盘 [px,py] = size(pop); totalfit = sum(fitvalue); p_fitvalue = fitvalue/totalfit; p_fitvalue = cumsum(p_fitvalue);%概率求和排序 ms = sort(rand(px,1));%从小到大排列 fitin = 1; newin = 1; while newin<=px if(ms(newin))<p_fitvalue(fitin) newpop(newin,:)=pop(fitin,:); newin = newin+1; else fitin=fitin+1; end end %交叉变换 %输入变量：pop：二进制的父代种群数，pc：交叉的概率 %输出变量：newpop：交叉后的种群数 function [newpop] = crossover(pop,pc) [px,py] = size(pop); newpop = ones(size(pop)); for i = 1:2:px-1 if(rand<pc) cpoint = round(rand*py); newpop(i,:) = [pop(i,1:cpoint),pop(i+1,cpoint+1:py)]; newpop(i+1,:) = [pop(i+1,1:cpoint),pop(i,cpoint+1:py)]; else newpop(i,:) = pop(i,:); newpop(i+1,:) = pop(i+1,:); end end %关于编译 %函数说明 %输入变量：pop：二进制种群，pm：变异概率 %输出变量：newpop变异以后的种群 function [newpop] = mutation(pop,pm) [px,py] = size(pop); newpop = ones(size(pop)); for i = 1:px if(rand<pm) mpoint = round(rand*py); if mpoint <= 0; mpoint = 1; end newpop(i,:) = pop(i,:); if newpop(i,mpoint) == 0 newpop(i,mpoint) = 1; else newpop(i,mpoint) == 1 newpop(i,mpoint) = 0; end else newpop(i,:) = pop(i,:); end end %求最优适应度函数 %输入变量：pop:种群，fitvalue:种群适应度 %输出变量：bestindividual:最佳个体，bestfit:最佳适应度值 function [bestindividual bestfit] = best(pop,fitvalue) [px,py] = size(pop); bestindividual = pop(1,:); bestfit = fitvalue(1); for i = 2:px if fitvalue(i)>bestfit bestindividual = pop(i,:); bestfit = fitvalue(i); end end