遺傳算法的過程在這里先不介紹了，可能在接下來的幾篇文章會介紹，這里介紹些實用的。

（1）Sheffield遺傳算法工具箱的安裝

我共享了下修改過文件名和后綴名的原版工具箱，地址為：http://pan.baidu.com/s/1inVKE

安裝方法：

將整個文件夾復制到matlab安裝文件夾中的toolbox文件夾

例如：C:\Program Files\MATLAB\R2013b\toolbox文件夾。

然后在Command Window里面輸入：

str = ['C:\Program Files\MATLAB\R2013b\toolbox\gatbx']addpath(str)

可能有些同學出現過這個問題，

Undefined function or method 'crtbp' for input arguments of type 'double'

或者是：

Cannot find an exact (case-sensitive) match for 'crtbp.m'
The closest match is C:\Program Files\MATLAB\R2012a\toolbox\gatbx\CRTBP.M
To change the file extension, cd to the file's folder, type:
?? movefile CRTBP.M CRTBP.m_bad; movefile CRTBP.m_bad CRTBP.m
and then cd back.

這是因為新舊Matlab版本對于M文件的文件名要求不盡相同，將其全部改為小寫文件名和文件后綴名即可。

（2）應用實例（1）——單變量函數求最值

類似模板的東西，求解此函數最大值：

有如下代碼：

%% Do Some Cleaning clc clear all; close all; %% Set the initial parameters lb = 1; ub = 2;%x belongs to [1,2] %% Plot figure(1); hold on; ezplot('sin(10*pi*X)/X',[lb,ub]); xlabel('x/X') ylabel('y/Y') %% Define the parameters of GA NIND = 40;%size of the group MAXGEN = 20;%max generations PRECI = 20;%length of a individual GGAP = 0.95;%gap px = 0.7;%the possibility of cross production pm = 0.1;%the possibility of mutation trace = zeros(2,MAXGEN);%init value of algorithm尋優函數 FieldD = [PRECI;lb;ub;1;0;1;1];%區域描述器 Chrom = crtbp(NIND,PRECI);%creat random discrete group %% Optimizations gen = 0;%counter of generations X = bs2rv(Chrom,FieldD);%bin to dec ObjV = sin(10 * pi * X) ./ X;%cal the f(x) while gen < MAXGENFitnV = ranking(ObjV);%allocate the adaptnessSelCh = select('sus',Chrom,FitnV,GGAP);%selectSelCh = recombin('xovsp',SelCh,px);%recombineSelCh = mut(SelCh, pm);%mutateX = bs2rv(SelCh,FieldD);%to decObjVSel = sin(10 * pi * X)./ X;%cal the next-gen's target f(x)[Chrom, ObjV] = reins(Chrom, SelCh, 1, 1, ObjV, ObjVSel);%reinsert them to father-genX = bs2rv(Chrom, FieldD);gen = gen + 1;%counter+=1%get every gen's answers and it's nums, Y stant for best f(x), I for%nums;[Y I] = min(ObjV);trace(1,gen) = X(I);trace(2,gen) = Y; end %% Plot plot(trace(1,:),trace(2,:),'bo');%plot every gen's answer grid on; plot(X,ObjV,'b*'); hold off %% Plot the evolution figure(2); plot(1:MAXGEN,trace(2,:)); grid on xlabel('count of generations') ylabel('answer') title('procedure') bestY = trace(2,end) bestX = trace(1,end)

運行結果：

（3）應用實例（2）——雙變量函數求最值

那么有如下代碼：

%% Do Some Cleaning clc clear all; close all; %% Set the initial parameters lbx = -2; ubx = 2;%x belongs to [-2,2] lby = -2; uby = 2;%y belongs to [-2,2] %% Plot figure(1); ezmesh('y*sin(2*pi*x) + x*cos(2*pi*y)',[lbx,ubx,lby,uby],50); xlabel('x/X') ylabel('y/Y') hold on; %% Define the parameters of GA NIND = 40;%size of the group MAXGEN = 20;%max generations PRECI = 20;%length of a individual GGAP = 0.95;%gap px = 0.7;%the possibility of cross production pm = 0.01;%the possibility of mutation trace = zeros(3,MAXGEN);%init value of algorithm尋優函數 FieldD = [PRECI PRECI;lbx lby;ubx uby;1 1;0 0;1 1;1 1];%Field discriber Chrom = crtbp(NIND,PRECI*2);%creat random discrete group %% Optimizations gen = 0;%counter of generations XY = bs2rv(Chrom,FieldD);%bin to dec X = XY(:,1); Y = XY(:,2); ObjV = Y.*sin(2*pi*X) + X.*cos(2*pi*Y);%cal the f(x,y) while gen < MAXGENFitnV = ranking(-ObjV);%allocate the adaptnessSelCh = select('sus',Chrom,FitnV,GGAP);%selectSelCh = recombin('xovsp',SelCh,px);%recombineSelCh = mut(SelCh, pm);%mutateXY = bs2rv(SelCh,FieldD);%bin to decX = XY(:,1);Y = XY(:,2);ObjVSel = Y.*sin(2*pi*X) + X.*cos(2*pi*Y);%cal the next-gen's target f(x)[Chrom, ObjV] = reins(Chrom, SelCh, 1, 1, ObjV, ObjVSel);%reinsert them to father-genXY = bs2rv(Chrom,FieldD);gen = gen + 1;%counter+=1%get every gen's answers and it's nums, Y stant for best f(x), I for%nums;[Y, I] = max(ObjV);trace(1:2,gen) = XY(I,:);trace(3,gen) = Y; end %% Plot plot3(trace(1,:),trace(2,:),trace(3,:),'bo');%plot every gen's answer grid on; plot3(XY(:,1),XY(:,2),ObjV,'b*'); hold off %% Plot the evolution figure(2); plot(1:MAXGEN,trace(3,:)); grid on xlabel('count of generations') ylabel('answer') title('procedure') bestX = trace(1,end) bestY = trace(2,end) bestZ = trace(3,end)

運行結果：

（4）應用實例（3）——遺傳算法接力優化（采用系統GAtool工具箱）

優化此函數：

第一個文件（主文件）：

%主程序：本程序采用遺傳算法接力進化， %將上次進化結束后得到的最終種群作為下次輸入的初始種群 clc; close all; clear all; %進化的代數 T=100; optionsOrigin=gaoptimset('Generations',T/2); [x,fval,reason,output,finnal_pop]=ga(@ff,2,optionsOrigin); %進行第二次接力進化 options1=gaoptimset('Generations',T/2,'InitialPopulation',finnal_pop,...'PlotFcns',@gaplotbestf); [x,fval,reason,output,finnal_pop]=ga(@ff,2,options1); Bestx=x BestFval=fval

評價函數文件，與主文件一起保存，名字為ff.m

%子函數：適應度函數同時也是目標函數,函數存儲名稱為ch14_2f.m function f=ff(x) g1=1.5+x(1)*x(2)-x(1)-x(2); g2=-x(1)*x(2); if(g1>0||g2>10)f=100; elsef=exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1); end

程序結果：

至此，第一部分結束，下一篇將介紹遺傳算法的基本內容。

轉載于:https://www.cnblogs.com/zhengnanlee/p/3439649.html

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