GA.zip_GA_GA-BP_GA优化BP_gooserbd_matlab

%% function [y,pop5]=GA(R,R_hang,x_up) %主程序 popsize = 20; %设置初始参数，群体大小 [chromlength,~] = size(R); %字符串长度（个体长度），染色体长度 pc = 0.8; %设置交叉概率 pm = 0.01; %设置变异概率 pop = initpop(popsize,chromlength); %运行初始化函数，随机产生初始群体 t = 20; %迭代次数 syms i for i = 1:t [objvalue,lu] = calobjvalue(pop,R,R_hang,x_up); %计算目标函数 fitvalue = calfitvalue(objvalue); %计算群体中每个个体的适应度 [newpop] = selection(pop,fitvalue); %复制 [newpop] = crossover(newpop,pc); %交叉 [newpop] = mutation(newpop,pm); %变异 [bestindividual,bestfit] = best(newpop,fitvalue,lu); %求出群体中适应值最大的个体以及适应度 y = min(bestfit); pop5 = bestindividual; pop = newpop; end % %% % function pop = initpop(popsize,chromlength) % pop = round (rand(popsize,chromlength)); % end % % %产生[2^n 2^(n-1)... 1]的行向量，然后求和，将二进制转化为十进制 % function pop2 = decodebinary(pop) % [px,py] = size(pop); % for i = 1:py % pop1(:,i) = 2.^(py-i).*pop(:,i); % end % pop2 = sum(pop1,2); %求每行的和 % end % %% % %二进制转十进制 % function pop2=decodechrom(pop,spoint,length) % pop1 = pop(:,spoint:spoint+length-1); % %取出第“spoint”位开始到第“spoint+length-1”位的参数 % pop2 = decodebinary(pop1); % %利用函数将二进制个体基因变成十进制 % end % % %实现目标函数的计算 % function [objvalue] = calobjvalue(pop) % temp1 = decodechrom(pop,1,8); %将pop每行转化成十进制 % x = temp1*10/1023; %将二值域中的数转化为变量域中的数 % objvalue = 11*sin(6*x)+7*cos(5*x); % end % %% % %计算个体的适应度 % function fitvalue = calfitvalue(objvalue) % Cmin = 0; % [px,py] = size(objvalue); % for i = 1:px % if objvalue(i)+Cmin>0 % temp = Cmin+objvalue(i); % else % temp=0.0; % end % fitvalue(i) = temp; % end % fitvalue = fitvalue'; % end % %% % %选择复制 % function [newpop] = selection(pop,fitvalue) % totalfit = sum(fitvalue); %适应值求和 % fitvalue = fitvalue/totalfit; %单个个体被选择的概率 % fitvalue = cumsum(fitvalue); % [px py] = size(pop); % ms = sort(rand(px,1)); %轮盘赌形式，从小到大排列随机数 % fitin = 1; %fitvalue(fitin)代表第fintin个个体的单个个体被选择的概率 % newin = 1; % while newin <= px % if(ms(newin))<fitvalue(fitin) % %ms(newin)表示的是ms列向量中的第“newin”位数值，小于fitvalue（fitin） % newpop(newin,:) = pop(fitin,:); % %赋值，即将旧种群中的第fitin个个体保留到下一代（newpop） % newin = newin+1; % else % fitin = fitin+1; % end % end % end % % %交叉 % 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 % end % % %变异 % function [newpop] = mutation(pop,pm) % [px,py] = size(pop); % newpop = ones(px,py); % for i = 1:px % if (rand<pm) % mpoint = round(rand*py); % if mpoint <= 0 % mpoint=1; % end % newpop(i,:) = pop(i,:); % if any(newpop(i,mpoint)) == 0 % newpop(i,mpoint) = 1; % else % newpop(i,mpoint) = 0; % end % else % newpop(i,:) = pop(i,:); % end % end % end % %% % %求出群体中最大的适应值及个体 % 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 % end % % %% % %计算最佳个体对应的X值 % function t = decodebinary2(pop) % [px,py]=size(pop); % for j = 1:py; % pop1(:,j) = 2.^(py-1).*pop(:,j); % py = py-1; % end % temp2 = sum(pop1,2); % t = temp2*2*pi/1023; % end % % end