%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 during 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 02 32 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 06 100 34 100 34 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 softwareyou can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundationeither version 1, or (at your option)
% any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTYwithout 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
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=c1c1(xZomeLength)=', evalFN '']
e2str=['x=c2c2(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 06 termOps(1) 34 termOps(1) 34 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 02 32 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 individuals in the pop
endPop = zeros(popSize,xZomeLength)%A secondary population matrix
c1 = zeros(1,xZomeLength) %An individual
c2 = zeros(1,xZomeLength) %An individual
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
是的。matlab是一个具有强大计算能力和仿真能力的数学软件，Matlab是一个具有强大计算能力和仿真能力的数软件，更多的侧重于科学计算不同于别的。
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