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 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 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=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];%似乎是默认变异概率
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];%似乎是默认交叉概率
end
end
if n<9 %Default select opts only i.e. roullete wheel.
selectOps=[];%若为轮盘的话,则选择概率为空,缺省值为轮盘选择
end
if n<8 %Default select info
selectFN=['normGeomSelect'];%正态几何分布
selectOps=[0.08];%为正态几何分布的参数
end
if n<6 %Default termination information
termOps=[100];%默认为100代
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
fp
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geneticalgorithm.rar_genetic algorithm
共89个文件
m:87个
ps:1个
readme:1个
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geneticalgorithm.rar (89个子文件)
遗传工具箱
genetic
sga.m 2KB
reclin.m 2KB
resplot.m 2KB
xovsprs.m 1KB
xovsp.m 1KB
xovdprs.m 1KB
recdis.m 2KB
xovmp.m 3KB
recombin.m 2KB
bs2rv.m 3KB
recmut.m 5KB
xovshrs.m 1KB
crtrp.m 2KB
migrate.m 7KB
sus.m 1KB
mutbga.m 5KB
crtbp.m 2KB
reins.m 5KB
content.m 2KB
rws.m 1KB
xovdp.m 1KB
select.m 2KB
recint.m 2KB
ranking.m 5KB
xovsh.m 1KB
objharv.m 2KB
crtbase.m 1KB
rep.m 1KB
mpga.m 4KB
mut.m 2KB
scaling.m 1KB
mutate.m 3KB
gaoto
arithXover.m 1KB
optMaxGenTerm.m 2KB
unifMutation.m 2KB
inversionMutation.m 2KB
shiftMutation.m 2KB
coranaMin.m 1KB
gaZBGradEval.m 684B
b2f.m 1KB
coranaEval.m 1KB
erXover.m 2KB
adjswapMutation.m 1KB
simpleXover.m 2KB
gaMichEval.m 193B
partmapXover.m 2KB
floatGradExample.m 2KB
uniformXover.m 2KB
calcbits.m 1KB
linerorderXover.m 2KB
binaryMutation.m 1KB
multiNonUnifMutation.m 2KB
coranaFeval.m 2KB
singleptXover.m 2KB
floatExample.m 2KB
gademo2.m 3KB
EER.m 2KB
tournSelect.m 2KB
gaZBGrad.m 174B
delta.m 1KB
normGeomSelect.m 2KB
tspEval.m 157B
gademo1.m 4KB
swapMutation.m 2KB
README 1KB
Contents.m 5KB
parse.m 1KB
gaotv5.ps 130KB
roulette.m 861B
binaryExample.m 2KB
maxGenTerm.m 1KB
f2b.m 1KB
ga.m 11KB
orderbasedXover.m 2KB
dists.m 7KB
threeswapMutation.m 1KB
boundaryMutation.m 2KB
initializega.m 1KB
startup.m 49B
cyclicXover.m 2KB
heuristicXover.m 2KB
gademo3.m 6KB
plotCorana.m 514B
enhancederXover.m 2KB
initializeoga.m 2KB
gademo1eval1.m 1KB
orderBasedExample.m 3KB
nonUnifMutation.m 2KB
gademo.m 4KB
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