遗传算法工具箱gaot

Ⅰ 请教怎么在matlab上安装遗传算法工具箱啊，为什么我安不上啊

安装步骤

1.将GAOT工具箱文件拷贝至Matlab文件夹下，具体路径为：
C:\program files\MATLAB\R2009a\toolbox。（也可以放在其他路径，不一定放在toolbox里面，比如C:\program files\MATLAB\R2009a也行）。

2.将GAOT工具箱路径加入Matlab文件路径之中。流程为：File-->Set Path-->Add with Subfolders。即，将C:\program files\MATLAB\R2009a\toolbox\gaot文件夹加入该路径系统中。

3. 重新启动Matlab，运行。

4. 但是在重新启动后，你会发现在命令窗口，会出现如下警告。即安装遗传工具箱后出现问题：Warning: Name is nonexistent or not a directory: \afs\eos\info\ie\ie589k_info\GAOT
解决方案：打开gaot文件夹下的startup.m，这里面写着“path(path,'\afs\eos\info\ie\ie589k_info\GAOT');”只要将“\afs\eos\info\ie\ie589k_info\GAOT”改为goat当前所在的目录，即“C:\program files\MATLAB\R2009a\toolbox\gaot”就可以了

5.测试是否安装成功，方法如下：在命令窗口输入：edit ga出现如下函数：function [x,fval,exitFlag,output,population,scores] = ga(fun,nvars,Aineq,bineq,Aeq,beq,lb,ub,nonlcon,options)此时你会发现这是MATLAB自带的ga函数，并不是你想要的工具箱中的ga函数，这样会在以后应用工具箱编写程序是发生错误。

6. 解决上述问题的方法：为了统一，不管你是高版本还是低版本，都可以通过将GAOT工具箱中的ga重命名为gaot_ga(名字可以随你定，但是不能改为大写GA，原因是MATLAB会默认大小写函数是同一个函数，不信你可以用edit ga和edit GA验证)，来实现解决上述问题。
这样整个的GAOT工具箱安装完备。

Ⅱ 遗传算法及matlab代码实现

深入探索遗传算法的世界，让我们通过MATLAB代码实现这一强大工具。遗传算法，如同自然界的演化过程，凭借其全局寻优和自适应特性，在无求导和连续性要求下寻求最优解。它的核心要素包括：基因型的二进制或浮点编码，通过适应度函数评价个体表现，以及一系列智能选择、交叉和变异操作。
想象一下，搜索问题就像袋鼠跳跃，遗传算法就像一次次的进化跳跃，不断接近山峰。每个个体代表一个可能的解，种群间的竞争与合作驱动着算法前进。从随机生成初始群体，到通过轮盘选择、交叉和变异产生新代，直到找到那颗璀璨的最优解星。
让我们以寻找最大值为例，MATLAB代码如下。初始化20个个体，每代迭代2000次，目标精度设定为0.01。二值化变量 [0,10] 到 [0,1023] 的转换，通过decodechrom函数巧妙实现。适应度计算函数 (calfitvalue.m) 设定目标函数：x+10*sin(5*x)+7*cos(4*x)，负目标值设为0，便于评估。
选择过程，selection.m 函数采用轮盘法则，根据个体的适应度比例随机选择。交叉操作在 crossover.m 中进行，以设定的概率进行单点交叉，生成新一代种群。而变异环节，mutation.m 负责在基因位上引入随机性，保持搜索的多样性。
在MATLAB的 gaot 工具箱中，这一切操作无缝集成，从种群初始化到优化迭代，再到最优解的输出和曲线绘制，每个步骤都精心设计，确保算法的高效运行。
现在，让我们一起驾驭遗传算法的力量，通过实践学习和理解这个强大的求解工具，在MATLAB的世界里探索无尽的可能性。在数乐君的引导下，欢迎同学们一起加入这个知识探索之旅，共同体验科学的魅力。

Ⅲ 运行遗基于遗传算法的BP神经网络MATLAB代码程序时总是出错！！！

这个问题也困扰了我好久，终于解决了。给你个ga.m程序，新建m文件复制进去，再运行程序试试。
%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