遺傳演算法工具箱gaot
Ⅰ 請教怎麼在matlab上安裝遺傳演算法工具箱啊，為什麼我安不上啊
安裝步驟
1.將GAOT工具箱文件拷貝至Matlab文件夾下，具體路徑為：
C:\program files\MATLAB\R2009a\toolbox。（也可以放在其他路徑，不一定放在toolbox裡面，比如C:\program files\MATLAB\R2009a也行）。
2.將GAOT工具箱路徑加入Matlab文件路徑之中。流程為：File-->Set Path-->Add with Subfolders。即，將C:\program files\MATLAB\R2009a\toolbox\gaot文件夾加入該路徑系統中。
3. 重新啟動Matlab，運行。
4. 但是在重新啟動後，你會發現在命令窗口，會出現如下警告。即安裝遺傳工具箱後出現問題：Warning: Name is nonexistent or not a directory: \afs\eos\info\ie\ie589k_info\GAOT
解決方案：打開gaot文件夾下的startup.m，這裡面寫著「path(path,'\afs\eos\info\ie\ie589k_info\GAOT');」只要將「\afs\eos\info\ie\ie589k_info\GAOT」改為goat當前所在的目錄，即「C:\program files\MATLAB\R2009a\toolbox\gaot」就可以了
5.測試是否安裝成功，方法如下：在命令窗口輸入：edit ga出現如下函數：function [x,fval,exitFlag,output,population,scores] = ga(fun,nvars,Aineq,bineq,Aeq,beq,lb,ub,nonlcon,options)此時你會發現這是MATLAB自帶的ga函數，並不是你想要的工具箱中的ga函數，這樣會在以後應用工具箱編寫程序是發生錯誤。
6. 解決上述問題的方法：為了統一，不管你是高版本還是低版本，都可以通過將GAOT工具箱中的ga重命名為gaot_ga(名字可以隨你定，但是不能改為大寫GA，原因是MATLAB會默認大小寫函數是同一個函數，不信你可以用edit ga和edit GA驗證)，來實現解決上述問題。
這樣整個的GAOT工具箱安裝完備。
Ⅱ 遺傳演算法及matlab代碼實現
深入探索遺傳演算法的世界，讓我們通過MATLAB代碼實現這一強大工具。遺傳演算法，如同自然界的演化過程，憑借其全局尋優和自適應特性，在無求導和連續性要求下尋求最優解。它的核心要素包括：基因型的二進制或浮點編碼，通過適應度函數評價個體表現，以及一系列智能選擇、交叉和變異操作。
想像一下，搜索問題就像袋鼠跳躍，遺傳演算法就像一次次的進化跳躍，不斷接近山峰。每個個體代表一個可能的解，種群間的競爭與合作驅動著演算法前進。從隨機生成初始群體，到通過輪盤選擇、交叉和變異產生新代，直到找到那顆璀璨的最優解星。
讓我們以尋找最大值為例，MATLAB代碼如下。初始化20個個體，每代迭代2000次，目標精度設定為0.01。二值化變數 [0,10] 到 [0,1023] 的轉換，通過decodechrom函數巧妙實現。適應度計算函數 (calfitvalue.m) 設定目標函數：x+10*sin(5*x)+7*cos(4*x)，負目標值設為0，便於評估。
選擇過程，selection.m 函數採用輪盤法則，根據個體的適應度比例隨機選擇。交叉操作在 crossover.m 中進行，以設定的概率進行單點交叉，生成新一代種群。而變異環節，mutation.m 負責在基因位上引入隨機性，保持搜索的多樣性。
在MATLAB的 gaot 工具箱中，這一切操作無縫集成，從種群初始化到優化迭代，再到最優解的輸出和曲線繪制，每個步驟都精心設計，確保演算法的高效運行。
現在，讓我們一起駕馭遺傳演算法的力量，通過實踐學習和理解這個強大的求解工具，在MATLAB的世界裡探索無盡的可能性。在數樂君的引導下，歡迎同學們一起加入這個知識探索之旅，共同體驗科學的魅力。
Ⅲ 運行遺基於遺傳演算法的BP神經網路MATLAB代碼程序時總是出錯！！！
這個問題也困擾了我好久，終於解決了。給你個ga.m程序，新建m文件復制進去，再運行程序試試。
%ga.m
function [x,endPop,bPop,traceInfo] = ga(bounds,evalFN,evalOps,startPop,opts,...
termFN,termOps,selectFN,selectOps,xOverFNs,xOverOps,mutFNs,mutOps)
% GA run a genetic algorithm
% function [x,endPop,bPop,traceInfo]=ga(bounds,evalFN,evalOps,startPop,opts,
% termFN,termOps,selectFN,selectOps,
% xOverFNs,xOverOps,mutFNs,mutOps)
%
% Output Arguments:
% x - the best solution found ring the course of the run
% endPop - the final population
% bPop - a trace of the best population
% traceInfo - a matrix of best and means of the ga for each generation
%
% Input Arguments:
% bounds - a matrix of upper and lower bounds on the variables
% evalFN - the name of the evaluation .m function
% evalOps - options to pass to the evaluation function ([NULL])
% startPop - a matrix of solutions that can be initialized
% from initialize.m
% opts - [epsilon prob_ops display] change required to consider two
% solutions different, prob_ops 0 if you want to apply the
% genetic operators probabilisticly to each solution, 1 if
% you are supplying a deterministic number of operator
% applications and display is 1 to output progress 0 for
% quiet. ([1e-6 1 0])
% termFN - name of the .m termination function (['maxGenTerm'])
% termOps - options string to be passed to the termination function
% ([100]).
% selectFN - name of the .m selection function (['normGeomSelect'])
% selectOpts - options string to be passed to select after
% select(pop,#,opts) ([0.08])
% xOverFNS - a string containing blank seperated names of Xover.m
% files (['arithXover heuristicXover simpleXover'])
% xOverOps - A matrix of options to pass to Xover.m files with the
% first column being the number of that xOver to perform
% similiarly for mutation ([2 0;2 3;2 0])
% mutFNs - a string containing blank seperated names of mutation.m
% files (['boundaryMutation multiNonUnifMutation ...
% nonUnifMutation unifMutation'])
% mutOps - A matrix of options to pass to Xover.m files with the
% first column being the number of that xOver to perform
% similiarly for mutation ([4 0 0;6 100 3;4 100 3;4 0 0])
% Binary and Real-Valued Simulation Evolution for Matlab
% Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay
%
% C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function
% optimization: A Matlab implementation. ACM Transactions on Mathmatical
% Software, Submitted 1996.
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 1, or (at your option)
% any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. A of the GNU
% General Public License can be obtained from the
% Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
%%$Log: ga.m,v $
%Revision 1.10 1996/02/02 15:03:00 jjoine
% Fixed the ordering of imput arguments in the comments to match
% the actual order in the ga function.
%
%Revision 1.9 1995/08/28 20:01:07 chouck
% Updated initialization parameters, updated mutation parameters to reflect
% b being the third option to the nonuniform mutations
%
%Revision 1.8 1995/08/10 12:59:49 jjoine
%Started Logfile to keep track of revisions
%
n=nargin;
if n<2 | n==6 | n==10 | n==12
disp('Insufficient arguements')
end
if n<3 %Default evalation opts.
evalOps=[];
end
if n<5
opts = [1e-6 1 0];
end
if isempty(opts)
opts = [1e-6 1 0];
end
if any(evalFN<48) %Not using a .m file
if opts(2)==1 %Float ga
e1str=['x=c1; c1(xZomeLength)=', evalFN ';'];
e2str=['x=c2; c2(xZomeLength)=', evalFN ';'];
else %Binary ga
e1str=['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=',...
evalFN ';'];
end
else %Are using a .m file
if opts(2)==1 %Float ga
e1str=['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);'];
e2str=['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);'];
else %Binary ga
e1str=['x=b2f(endPop(j,:),bounds,bits);[x v]=' evalFN ...
'(x,[gen evalOps]); endPop(j,:)=[f2b(x,bounds,bits) v];'];
end
end
if n<6 %Default termination information
termOps=[100];
termFN='maxGenTerm';
end
if n<12 %Default muatation information
if opts(2)==1 %Float GA
mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation'];
mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 0];
else %Binary GA
mutFNs=['binaryMutation'];
mutOps=[0.05];
end
end
if n<10 %Default crossover information
if opts(2)==1 %Float GA
xOverFNs=['arithXover heuristicXover simpleXover'];
xOverOps=[2 0;2 3;2 0];
else %Binary GA
xOverFNs=['simpleXover'];
xOverOps=[0.6];
end
end
if n<9 %Default select opts only i.e. roullete wheel.
selectOps=[];
end
if n<8 %Default select info
selectFN=['normGeomSelect'];
selectOps=[0.08];
end
if n<6 %Default termination information
termOps=[100];
termFN='maxGenTerm';
end
if n<4 %No starting population passed given
startPop=[];
end
if isempty(startPop) %Generate a population at random
%startPop=zeros(80,size(bounds,1)+1);
startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));
end
if opts(2)==0 %binary
bits=calcbits(bounds,opts(1));
end
xOverFNs=parse(xOverFNs);
mutFNs=parse(mutFNs);
xZomeLength = size(startPop,2); %Length of the xzome=numVars+fittness
numVar = xZomeLength-1; %Number of variables
popSize = size(startPop,1); %Number of indivials in the pop
endPop = zeros(popSize,xZomeLength); %A secondary population matrix
c1 = zeros(1,xZomeLength); %An indivial
c2 = zeros(1,xZomeLength); %An indivial
numXOvers = size(xOverFNs,1); %Number of Crossover operators
numMuts = size(mutFNs,1); %Number of Mutation operators
epsilon = opts(1); %Threshold for two fittness to differ
oval = max(startPop(:,xZomeLength)); %Best value in start pop
bFoundIn = 1; %Number of times best has changed
done = 0; %Done with simulated evolution
gen = 1; %Current Generation Number
collectTrace = (nargout>3); %Should we collect info every gen
floatGA = opts(2)==1; %Probabilistic application of ops
display = opts(3); %Display progress
while(~done)
%Elitist Model
[bval,bindx] = max(startPop(:,xZomeLength)); %Best of current pop
best = startPop(bindx,:);
if collectTrace
traceInfo(gen,1)=gen; %current generation
traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness
traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness
traceInfo(gen,4)=std(startPop(:,xZomeLength));
end
if ( (abs(bval - oval)>epsilon) | (gen==1)) %If we have a new best sol
if display
fprintf(1,'\n%d %f\n',gen,bval); %Update the display
end
if floatGA
bPop(bFoundIn,:)=[gen startPop(bindx,:)]; %Update bPop Matrix
else
bPop(bFoundIn,:)=[gen b2f(startPop(bindx,1:numVar),bounds,bits)...
startPop(bindx,xZomeLength)];
end
bFoundIn=bFoundIn+1; %Update number of changes
oval=bval; %Update the best val
else
if display
fprintf(1,'%d ',gen); %Otherwise just update num gen
end
end
endPop = feval(selectFN,startPop,[gen selectOps]); %Select
if floatGA %Running with the model where the parameters are numbers of ops
for i=1:numXOvers,
for j=1:xOverOps(i,1),
a = round(rand*(popSize-1)+1); %Pick a parent
b = round(rand*(popSize-1)+1); %Pick another parent
xN=deblank(xOverFNs(i,:)); %Get the name of crossover function
[c1 c2] = feval(xN,endPop(a,:),endPop(b,:),bounds,[gen xOverOps(i,:)]);
if c1(1:numVar)==endPop(a,(1:numVar)) %Make sure we created a new
c1(xZomeLength)=endPop(a,xZomeLength); %solution before evaluating
elseif c1(1:numVar)==endPop(b,(1:numVar))
c1(xZomeLength)=endPop(b,xZomeLength);
else
%[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);
eval(e1str);
end
if c2(1:numVar)==endPop(a,(1:numVar))
c2(xZomeLength)=endPop(a,xZomeLength);
elseif c2(1:numVar)==endPop(b,(1:numVar))
c2(xZomeLength)=endPop(b,xZomeLength);
else
%[c2(xZomeLength) c2] = feval(evalFN,c2,[gen evalOps]);
eval(e2str);
end
endPop(a,:)=c1;
endPop(b,:)=c2;
end
end
for i=1:numMuts,
for j=1:mutOps(i,1),
a = round(rand*(popSize-1)+1);
c1 = feval(deblank(mutFNs(i,:)),endPop(a,:),bounds,[gen mutOps(i,:)]);
if c1(1:numVar)==endPop(a,(1:numVar))
c1(xZomeLength)=endPop(a,xZomeLength);
else
%[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);
eval(e1str);
end
endPop(a,:)=c1;
end
end
else %We are running a probabilistic model of genetic operators
for i=1:numXOvers,
xN=deblank(xOverFNs(i,:)); %Get the name of crossover function
cp=find(rand(popSize,1)<xOverOps(i,1)==1);
if rem(size(cp,1),2) cp=cp(1:(size(cp,1)-1)); end
cp=reshape(cp,size(cp,1)/2,2);
for j=1:size(cp,1)
a=cp(j,1); b=cp(j,2);
[endPop(a,:) endPop(b,:)] = feval(xN,endPop(a,:),endPop(b,:),...
bounds,[gen xOverOps(i,:)]);
end
end
for i=1:numMuts
mN=deblank(mutFNs(i,:));
for j=1:popSize
endPop(j,:) = feval(mN,endPop(j,:),bounds,[gen mutOps(i,:)]);
eval(e1str);
end
end
end
gen=gen+1;
done=feval(termFN,[gen termOps],bPop,endPop); %See if the ga is done
startPop=endPop; %Swap the populations
[bval,bindx] = min(startPop(:,xZomeLength)); %Keep the best solution
startPop(bindx,:) = best; %replace it with the worst
end
[bval,bindx] = max(startPop(:,xZomeLength));
if display
fprintf(1,'\n%d %f\n',gen,bval);
end
x=startPop(bindx,:);
if opts(2)==0 %binary
x=b2f(x,bounds,bits);
bPop(bFoundIn,:)=[gen b2f(startPop(bindx,1:numVar),bounds,bits)...
startPop(bindx,xZomeLength)];
else
bPop(bFoundIn,:)=[gen startPop(bindx,:)];
end
if collectTrace
traceInfo(gen,1)=gen; %current generation
traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness
traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness
end