遗传算法工具箱(GOAT)的注释
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 during the course of the run 求得的最优解
% endPop - the final population 最终得到的种群
% bPop - a trace of the best population 最优种群的一个搜索轨迹
% traceInfo - a matrix of best and means of the ga for each generation 每一代种群的最优个体和均值
%
% Input Arguments:
% bounds - a matrix of upper and lower bounds on the variables 代表变量上下界的矩阵
% evalFN - the name of the evaluation .m function 适应度函数所在的m文件
% evalOps - options to pass to the evaluation function ([NULL]) 传递给适应度函数的参数
% startPop - a matrix of solutions that can be initialized 初始种群
% 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])
% 1e-6：变量进行二进制编码时指定的精度，1：选择浮点编码，0：为二进制编码,并指定精度，为第三个0时结束
% termFN - name of the .m termination function (['maxGenTerm']) 终止函数所在的m文件
% termOps - options string to be passed to the termination function 传递个终止函数的参数，一般为需要遗传的代数
% ([100]).
% selectFN - name of the .m selection function (['normGeomSelect']) 选择函数的名称
% selectOpts - options string to be passed to select after 传递给选择函数的参数，一般为变异概率
% select(pop,#,opts) ([0.08])
% xOverFNS - a string containing blank seperated names of Xover.m 交叉函数名称表，以空格分开
% files (['arithXover heuristicXover simpleXover'])
% xOverOps - A matrix of options to pass to Xover.m files with the 传递给交叉函数的参数表
% 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 变异函数表% files (['boundaryMutation multiNonUnifMutation ...
% nonUnifMutation unifMutation'])
% mutOps - A matrix of options to pass to Xover.m files with the 传递给变异函数的参数表
% 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 copy 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;%nargin得到函数的参数个数
if n<2 | n==6 | n==10 | n==12
disp('Insufficient arguements')
end
if n<3 %Default evalation opts.
evalOps=[];%适应度函数的参数为空
end
if n<5
opts = [1e-6 1 0];%进行二进制编码，并指定精度
end
if isempty(opts)
opts = [1e-6 1 0];%默认情况下为二进制编码
end
if any(evalFN<48) %Not using a .m file
if opts(2)==1 %Float ga,opts函数的第二个参数为1，则使用浮点数编码
e1str=['x=c1; c1(xZomeLength)=', evalFN ';'];
e2str=['x=c2; c2(xZomeLength)=', evalFN ';'];
else %Binary ga，opts函数的第二个参数为0，则使用二进制编码
e1str=['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=',...
evalFN ';'];
end
else %Are using a .m file
if opts(2)==1 %Float ga，opts函数的第二个参数为1，则使用浮点数编码
e1str=['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);'];
e2str=['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);'];
else %Binary ga，opts函数的第二个参数为0，则使用二进制编码
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，默认终止函数信息
termOps=[100];%默认遗传100代
termFN='maxGenTerm';
end
if n<12 %Default muatation information，默认变异信息
if opts(2)==1 %Float GA，浮点数编码
mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation'];
mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 0];
else %Binary GA，二进制编码
mutFNs=['binaryMutation'];%采用二进制变异
mutOps=[0.05];%默认变异率为0.05
end
end
if n<10 %Default crossover information，默认交叉信息
if opts(2)==1 %Float GA，浮点数编码
xOverFNs=['arithXover heuristicXover simpleXover'];
xOverOps=[2 0;2 3;2 0];
else %Binary GA，二进制编码
xOverFNs=['simpleXover'];
xOverOps=[0.6];
end
end
if n<9 %Default select opts only i.e. roullete wheel.默认为轮盘赌选择算法
selectOps=[];
end
if n<8 %Default select info
selectFN=['normGeomSelect'];
selectOps=[0.08];
end
if n<6 %Default termination information，默认终止函数信息
termOps=[100];%默认遗传100代
termFN='maxGenTerm';
end
if n<4 %No starting population passed given，没有给定初始化的种群
startPop=[];
end
if isempty(startPop) %Generate a population at random，如果没有给定初始种群，则随机生成
%startPop=zeros(80,size(bounds,1)+1);
startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));%生成初始化的种群
end
if opts(2)==0 %binary,二进制编码
bits=calcbits(bounds,opts(1));%找出在bounds范围内满足精度的bit位数
end
xOverFNs=parse(xOverFNs);%分析交叉函数
mutFNs=parse(mutFNs);%分析变异函数
xZomeLength = size(startPop,2); %Length of the xzome=numVars+fittness，返回个体的变量数目加上适值所占一位
numVar = xZomeLength-1; %Number of variables，得到变量数目
popSize = size(startPop,1); %Number of individuals in the pop，得到个体大小
endPop = zeros(popSize,xZomeLength); %A secondary population matrix，下一代种群矩阵
c1 = zeros(1,xZomeLength); %An individual，得到一个个体，为交叉准备
c2 = zeros(1,xZomeLength); %An individual，得到另一个个体，为交叉准备
numXOvers = size(xOverFNs,1); %Number of Crossover operators，交叉操作的数目
numMuts = size(mutFNs,1); %Number of Mutation operators，变异操作数目
epsilon = opts(1); %Threshold for two fittness to differ，表示二进制的精度
oval = max(startPop(:,xZomeLength)); %Best value in start pop，适值最大的个体，也就是最后好的个体在一代中
bFoundIn = 1; %Number of times best has changed，最优个体改变的数目
done = 0; %Done with simulated evolution，进化是否结束的标志
gen = 1; %Current Generation Number，当前的种群数
collectTrace = (nargout>3); %Should we collect info every gen，是否每代都进行信息收集
floatGA = opts(2)==1; %Probabilistic application of ops，是否是浮点编码
display = opts(3); %Display progress，显示过程
while(~done) %当模拟进化没有进行的时候
%Elitist Model
[bval,bindx] = max(startPop(:,xZomeLength)); %Best of current pop，得到当前种群中最优的个体
best = startPop(bindx,:);
if collectTrace %如果需要收集数据
traceInfo(gen,1)=gen; %current generation，当前代数
traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness，当前最优的适值
traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness，当前代中平均适值
traceInfo(gen,4)=std(startPop(:,xZomeLength)); %当前代中适值的标准差
end
if ( (abs(bval - oval)>epsilon) | (gen==1)) %If we have a new best sol，如果产生了新的更优的个体，或者是第一代计算
if display %如果需要显示计算过程
fprintf(1,'\n%d %f\n',gen,bval); %Update the display，显示当前的代数和最优适值
end
if floatGA
bPop(bFoundIn,:)=[gen startPop(bindx,:)]; %Update bPop Matrix，同时改变最优的适值和个体，并记录
else
bPop(bFoundIn,:)=[gen b2f(startPop(bindx,1:numVar),bounds,bits)...
startPop(bindx,xZomeLength)]; %返回最优个体和最优值的浮点数信息
end
bFoundIn=bFoundIn+1; %Update number of changes，增加一次改变
oval=bval; %Update the best val，更新最优的适值
else
if display
fprintf(1,'%d ',gen); %Otherwise just update num gen，仅仅显示当前的代数
end
end
endPop = feval(selectFN,startPop,[gen selectOps]); %Select，通过选择，得到下一代的种群
if floatGA %Running with the model where the parameters are numbers of ops
for i=1:numXOvers, %对每一个交叉函数
for j=1:xOverOps(i,1),
a = round(rand*(popSize-1)+1); %Pick a parent，随机得到交叉的父代
b = round(rand*(popSize-1)+1); %Pick another parent
xN=deblank(xOverFNs(i,:)); %Get the name of crossover function，得到要交叉的函数
[c1 c2] = feval(xN,endPop(a,:),endPop(b,:),bounds,[gen xOverOps(i,:)]); %通过交叉，得到新的两个个体
if c1(1:numVar)==endPop(a,(1:numVar)) %Make sure we created a new %确保得到的个体区别于父体，如果相同，直接将适值赋值
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,[genevalOps]);，如果不相同，得到个体，并计算适值函数值
eval(e1str);
end
if c2(1:numVar)==endPop(a,(1:numVar)) %另一个个体的操作同上
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; %用新的个体代替原有的个体
endPop(b,:)=c2;
end
end
for i=1:numMuts, %对每一个变异函数
for j=1:mutOps(i,1),
a = round(rand*(popSize-1)+1); %随机产生变异的个体
c1 = feval(deblank(mutFNs(i,:)),endPop(a,:),bounds,[gen mutOps(i,:)]); %对每个函数，将选择变异的个体进行变异
if c1(1:numVar)==endPop(a,(1:numVar)) %确保产生了新的个体
c1(xZomeLength)=endPop(a,xZomeLength);
else
%[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);
eval(e1str); %得到新的个体，并得到适值
end
endPop(a,:)=c1; %用新的个体代替原有的个体
end
end
else %We are running a probabilistic model of genetic operators，在概率下进行交叉
for i=1:numXOvers, %对每一个交叉函数运算
xN=deblank(xOverFNs(i,:)); %Get the name of crossover function，得到交叉函数
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 %对每一个变异操作
mN=deblank(mutFNs(i,:)); %得到变异函数
for j=1:popSize
endPop(j,:) = feval(mN,endPop(j,:),bounds,[gen mutOps(i,:)]); %变异并产生适值
eval(e1str);
end
end
end
gen=gen+1; %代数加一
done=feval(termFN,[gen termOps],bPop,endPop); %See if the ga is done，是否满足结束条件
startPop=endPop; %Swap the populations，替换新的种群
[bval,bindx] = min(startPop(:,xZomeLength)); %Keep the best solution
startPop(bindx,:) = best; %replace it with the worst
end
[bval,bindx] = max(startPop(:,xZomeLength)); %得到最优的个体，并显示
if display
fprintf(1,'\n%d %f\n',gen,bval);
end
x=startPop(bindx,:); %得到当代的最优解
if opts(2)==0 %binary，如果是二进制编码，得到他的浮点数表示形式
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 %记录下来遗传的轨迹
traceInfo(gen,1)=gen; %current generation
traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness
traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness
end
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