Upload original Matlab程序设计 course files (part 8)

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/README

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-version https://git-lfs.github.com/spec/v1
-oid sha256:0a5a962f58bf5ee878c01fb8f15e76b01982a334df8dc9f94d2c313693031915
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+This directory contains the Genetic Algorithm Optimization Toolbox for
+Matlab 5.
+
+To use this, if you are local to NCSU and have AFS access to this
+directory, simply extend the matlab path using the following command.
+You can also place this command in a file called startup.m.  Everytime
+you start Matlab in the directory containing this file, the path will
+always be extended.
+
+>>path(path,'/afs/eos/service/ie/research/kay_res/GAToolBox/gaot');
+
+Otherwise, install the .m files into a directory named gaot and extend
+the matlab path to that directory.  The compressed tar archive and the
+zip file should automatically create the gaot directory for you.
+
+The companion paper describing this toolbox is included here as
+gaotv5.ps. The paper, the three demo files, gademo1.m gademo2.m
+gademo3.m, and four example scripts, binaryExample, floatExample,
+floatGradExample, and orderbasedExample, should be sufficient
+explanation of this toolbox.  For any questions, comments, suggestions
+send mail to jjoine@eos.ncsu.edu.
+
+For a list of the files in the tool box get help on gaot.
+
+Thanks for trying the toolbox.
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/heuristicXover.m

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-size 2136
+function [c1,c2] = heuristicXover(p1,p2,bounds,Ops)
+% Heuristic crossover takes two parents P1,P2 and performs an extrapolation
+% along the line formed by the two parents outward in the direction of the
+% better parent.
+%
+% function [c1,c2] = heuristicXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for heuristic crossover, [gen #heurXovers number_of_retries]
+
+% 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.
+
+retry=Ops(3); 				% Number of retries
+i=0;
+good=0;
+b1=bounds(:,1)';
+b2=bounds(:,2)';
+numVar = size(p1,2)-1;
+% Determine the best and worst parent
+if(p1(numVar+1) > p2(numVar+1))
+  bt = p1; 
+  wt = p2;
+else
+  bt = p2;
+  wt = p1;
+end
+while i<retry
+  % Pick a random mix amount
+  a = rand;
+  % Create the child
+  c1 = a * (bt - wt) + bt;
+  
+  % Check to see if child is within bounds
+  if (c1(1:numVar) <= b2 & (c1(1:numVar) >= b1))
+    i = retry;
+    good=1;
+  else
+    i = i + 1;
+  end
+end
+
+% If new child is not feasible just return the new children
+if(~good) 
+  c1 = wt;
+end
+
+% Crossover functions return two children therefore return the best
+% and the new child created
+c2 = bt;
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/initializega.m

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-oid sha256:1e90c7696637d919c8915c8b70e69dc2c399c2a13e26eaa42a40ec13e5742570
-size 1177
+function [pop] = initializega(num, bounds, evalFN,evalOps,options)
+
+if nargin<5
+  options=[1e-6 1];
+end
+if nargin<4
+  evalOps=[];
+end
+
+if any(evalFN<48) 
+  if options(2)==1 %Float GA
+    estr=['x=pop(i,1); pop(i,xZomeLength)=', evalFN ';'];  
+  else %Binary GA
+    estr=['x=b2f(pop(i,:),bounds,bits); pop(i,xZomeLength)=', evalFN ';']; 
+  end
+else %A .m file
+  if options(2)==1 %Float GA
+    estr=['[ pop(i,:) pop(i,xZomeLength)]=' evalFN '(pop(i,:),[0 evalOps]);']; 
+  else %Binary GA
+    estr=['x=b2f(pop(i,:),bounds,bits);[x v]=' evalFN ...
+	'(x,[0 evalOps]); pop(i,:)=[f2b(x,bounds,bits) v];'];  
+    end
+end
+
+
+numVars     = size(bounds,1); 		%Number of variables
+rng         = (bounds(:,2)-bounds(:,1))'; %The variable ranges'
+
+if options(2)==1 %Float GA
+  xZomeLength = numVars+1; 		%Length of string is numVar + fit
+  pop         = zeros(num,xZomeLength); 	%Allocate the new population
+  pop(:,1:numVars)=(ones(num,1)*rng).*(rand(num,numVars))+...
+    (ones(num,1)*bounds(:,1)');
+else %Binary GA
+  bits=calcbits(bounds,options(1));
+  xZomeLength = sum(bits)+1; 		%Length of string is numVar + fit
+  pop = round(rand(num,sum(bits)+1));
+end
+
+for i=1:num
+  eval(estr);
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/initializeoga.m

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-oid sha256:63782fbf1de94314e1f4fa41be61d7ff908f85fe7bc234cb6e5aee91c9a93a27
-size 1561
+function [pop] = initializeoga(num,bounds,evalFN,evalOps,options)
+% initializeoga(populationSize, variableBounds,evalFN,evalOps,options)
+%    initializeoga creates a matrix of random permutations with 
+%    a number of rows equal to the populationSize and a number
+%    columns equal to the size of the permutation plus 1 for 
+%    the f(x) value which is found by applying the evalFN.
+%    This initization function is used with an order-based
+%    representation.
+%
+% pop    - the initial, evaluated, random population 
+% num    - the size of the population, i.e. the number to create
+% bounds - the number of permutations in an individual (e.g., number
+%          of cities in a tsp
+% evalFN - the evaluation fn, usually the name of the .m file for evaluation
+% evalOps- any options to be passed to the eval function defaults []
+% options- options to the initialize function, ie. [eps float/binary prec] 
+%           where eps is the epsilon value and the second option is 1 for
+%          orderOps, prec is the precision of the variables defaults [1e-6 1]
+
+if nargin<5
+  options=[1e-6 1];
+end
+if nargin<4
+  evalOps=[];
+end
+
+if any(evalFN<48) %Not a .m file
+  estr=['x=pop(i,:); pop(i,xZomeLength)=', evalFN ';'];  
+else %A .m file
+    estr=['[pop(i,:) pop(i,xZomeLength)]=' evalFN '(pop(i,:),[0 evalOps]);'];  
+end
+
+
+numVars     = bounds; 		%Number of variables
+
+xZomeLength = numVars+1; 		%Length of string is numVar + fit
+pop         = zeros(num,xZomeLength); 	%Allocate the new population
+for i=1:num
+  pop(i,:)=[randperm(numVars) 0];
+  eval(estr);
+end
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/inversionMutation.m

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+function [child] = inversionMutation(par,bounds,genInfo,Ops)
+% Inversion mutation  inverts the bits or permutation between two
+% cut points.
+%
+% function [newSol] = inversionMutation(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for binaryMutation [gen prob_of_mutation]
+
+% 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.
+
+sz = size(par,2)-1;
+pos1 = round(rand*sz + 0.5);
+pos2 = round(rand*sz + 0.5);
+if pos1 ~= pos2
+   if pos1 > pos2
+      t = pos1; pos1 = pos2; pos2 = t;
+   end
+   child = [par(1:pos1-1) fliplr(par(pos1:pos2)) par(pos2+1:(sz+1))];
+else
+   child = par;
+end
+    
+   

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/linerorderXover.m

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-size 1850
+function [c1,c2]=lox(p1,p2,bounds,genInfo,ops)
+% Linearorder crossover takes two parents P1,P2 and performs linear order
+% crossover for permutation strings.  
+%
+% function [c1,c2] = linearOrderXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+sz=size(p1,2)-1;
+c1=p1(1:sz);%zeros(1,sz);
+c2=p2(1:sz);%zeros(1,sz);
+cut1=round(rand*(sz-1)+1.5);
+cut2=round(rand*(sz-1)+1.5);
+while cut2 == cut1
+   cut2 = round(rand*sz + 0.5);
+end
+if cut1 > cut2
+   t = cut1; cut1 = cut2; cut2 = t;
+end
+for i=cut1:cut2
+  c1=strrep(c1,p2(i),-1);
+  c2=strrep(c2,p1(i),-1);
+end
+g1=find(c1>-1);
+g2=find(c2>-1);
+c1=[c1(g1(1:(cut1-1))) p2(cut1:cut2) c1(g1(cut1:end)) p1(end)];
+c2=[c2(g2(1:(cut1-1))) p1(cut1:cut2) c2(g2(cut1:end)) p2(end)];
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/maxGenTerm.m

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-size 1345
+function [done] = maxGenTerm(ops,bPop,endPop)
+% function [done] = maxGenTerm(ops,bPop,endPop)
+%
+% Returns 1, i.e. terminates the GA when the maximal_generation is reached.
+%
+% ops    - a vector of options [current_gen maximum_generation]
+% bPop   - a matrix of best solutions [generation_found solution_string]
+% endPop - the current generation of solutions
+
+% 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.
+
+currentGen = ops(1);
+maxGen     = ops(2);
+done       = currentGen >= maxGen; 

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/multiNonUnifMutation.m

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-version https://git-lfs.github.com/spec/v1
-oid sha256:63cb60366f073527d546094319f0769a62a191bb4568620626638e886fee67af
-size 1991
+function [parent] = multiNonUnifMutation(parent,bounds,Ops)
+% Multi-Non uniform mutation changes all of the parameters of the parent
+% based on a non-uniform probability distribution.  This Gaussian
+% distribution starts wide, and narrows to a point distribution as the
+% current generation approaches the maximum generation.
+%
+% function [newSol] = multiNonUnifMutate(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for multiNonUnifMutation 
+%          [gen #MultiNonUnifMutations maxGen b]
+
+% 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.
+
+cg=Ops(1); 				% Current Generation
+mg=Ops(3); 				% Maximum Number of Generations
+b=Ops(4);                               % Shape parameter
+df = bounds(:,2) - bounds(:,1); 	% Range of the variables
+numVar = size(parent,2)-1; 		% Get the number of variables
+% Now mutate that point
+md = round(rand(1,numVar));
+for i = 1:numVar
+  if md(i)
+    parent(i)=parent(i)+delta(cg,mg,bounds(i,2)-parent(i),b);
+  else
+    parent(i)=parent(i)-delta(cg,mg,parent(i)-bounds(i,1),b);
+  end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/nonUnifMutation.m

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-oid sha256:d209a69a7dc79798aa290ef18d06a0ce4691a29d15261925eeb00a593789ee57
-size 2190
+function [parent] = nonUnifMutate(parent,bounds,Ops)
+% Non uniform mutation changes one of the parameters of the parent
+% based on a non-uniform probability distribution.  This Gaussian
+% distribution starts wide, and narrows to a point distribution as the
+% current generation approaches the maximum generation.
+%
+% function [newSol] = multiNonUnifMutate(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for nonUnifMutate[gen #NonUnifMutations maxGen b]
+
+% 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.
+
+cg=Ops(1); 				% Current Generation
+mg=Ops(3);                              % Maximum Number of Generations
+b=Ops(4);                               % Shape parameter
+df = bounds(:,2) - bounds(:,1); 	% Range of the variables
+numVar = size(parent,2)-1; 		% Get the number of variables
+% Pick a variable to mutate randomly from 1 to number of vars
+mPoint = round(rand * (numVar-1)) + 1;
+md = round(rand); 			% Choose a direction of mutation
+if md 					% Mutate towards upper bound
+  newValue=parent(mPoint)+delta(cg,mg,bounds(mPoint,2)-parent(mPoint),b);
+else 					% Mutate towards lower bound
+  newValue=parent(mPoint)-delta(cg,mg,parent(mPoint)-bounds(mPoint,1),b);
+end
+parent(mPoint) = newValue; 		% Make the child
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/normGeomSelect.m

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-version https://git-lfs.github.com/spec/v1
-oid sha256:c55cae033e91816ea78cd174bb245c1fb8719c31a0107d5593594fdf06dfcf8b
-size 2308
+function[newPop] = normGeomSelect(oldPop,options)
+% NormGeomSelect is a ranking selection function based on the normalized
+% geometric distribution.  
+%
+% function[newPop] = normGeomSelect(oldPop,options)
+% newPop  - the new population selected from the oldPop
+% oldPop  - the current population
+% options - options to normGeomSelect [gen probability_of_selecting_best]
+
+% 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.
+
+q=options(2); 				% Probability of selecting the best
+e = size(oldPop,2); 			% Length of xZome, i.e. numvars+fit
+n = size(oldPop,1); 			% Number of individuals in pop
+newPop = zeros(n,e); 			% Allocate space for return pop
+fit = zeros(n,1); 			% Allocates space for prob of select
+x=zeros(n,2); 			        % Sorted list of rank and id
+x(:,1) =[n:-1:1]'; 			% To know what element it was
+[y x(:,2)] = sort(oldPop(:,e)); 	% Get the index after a sort
+r = q/(1-(1-q)^n); 			% Normalize the distribution, q prime
+fit(x(:,2))=r*(1-q).^(x(:,1)-1); 	% Generates Prob of selection 
+fit = cumsum(fit); 			% Calculate the cumulative prob. func
+rNums=sort(rand(n,1)); 			% Generate n sorted random numbers
+fitIn=1; newIn=1; 			% Initialize loop control
+while newIn<=n 				% Get n new individuals
+  if(rNums(newIn)<fit(fitIn)) 		
+    newPop(newIn,:) = oldPop(fitIn,:); 	% Select the fitIn individual 
+    newIn = newIn+1; 			% Looking for next new individual
+  else
+    fitIn = fitIn + 1; 			% Looking at next potential selection
+  end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/optMaxGenTerm.m

@@ -1,3 +1,36 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0569b5f983163b4e8d2c616a26581b630bd86d85580abfd92ab0edd5ce25e30a
-size 1555
+function [done] = optMaxGenTerm(ops,bPop,endPop)
+% function [done] = optMaxGenTerm(ops,bPop,endPop)
+%
+% Returns 1, i.e. terminates the GA, when either the maximal_generation is
+% reached or when the optimal function val is found.
+%
+% ops    - a vector of options [maximum_generation optimal epsilon]
+% bPop   - a matrix of best solutions [generation_found solution_string]
+% endPop - the current generation of solutions
+
+% 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.
+
+currentGen = ops(1);
+maxGen     = ops(2);
+optimal    = ops(3);
+epsilon    = ops(4);
+fitIndex   = size(endPop,2);
+bestSolVal = max(endPop(:,fitIndex));
+done       = (currentGen >= maxGen) | ((optimal - bestSolVal) <= epsilon);

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/orderBasedExample.m

@@ -1,3 +1,131 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4ab089b8b735270aa6498c783a6f8b0cc7ea7da1c6baa6127f2ad466113eaab6
-size 2859
+echo on
+% This script shows how to use the ga using an order-based representation. 
+% You should see the demos for
+% more information as well. gademo1, gademo2, gademo3
+global distMatrix
+% Setting the seed to the same for binary
+rand('seed',156789)
+
+% 6 city problem
+t=[ 92.6112 59.0801; 49.1155 50.0000; 12.5000 57.9436; 75.0000 19.3703;
+  8.6504 13.7113; 36.8786 92.9628];
+t=100*rand(15,2);
+sz=size(t,1);
+distMatrix=dists(t,t);
+
+
+% Order-based Representation Crossover Operators
+% cyclicXover.m          
+% erXover.m              
+% enhancederXover.m      
+% linerorderXover.m      
+% orderbasedXover.m      
+% partmapXover.m         
+% singleptXover.m        
+% uniformXover.m         
+
+xFns = 'cyclicXover uniformXover partmapXover orderbasedXover '
+xFns =[xFns,'singleptXover linerorderXover'];
+% xFns = [xFns,'enhancederXover linerorderXover']
+% xFns = [xFns,'linerorderXover singleptXover']
+xOpts = [2;2;2;2;2;2];% 2; 2; 2; 2; 2; 2; 2];
+
+% Order-based Mutation Operators
+% inversionMutation
+% adjswapMutation.m      
+% shiftMutation.m        
+% swapMutation.m         
+% threeswapMutation.m    
+
+mFns = 'inversionMutation adjswapMutation shiftMutation swapMutation threeswapMutation';
+mOpts = [2;2;2;2;2];
+
+% Termination Operators
+termFns = 'maxGenTerm';
+termOps = [100]; % 200 Generations
+
+% Selection Function
+selectFn = 'normGeomSelect';
+selectOps = [0.08];
+
+% Evaluation Function
+evalFn = 'tspEval';
+evalOps = [];
+
+type tspEval
+
+% Bounds on the number of cities in the TSP
+bounds = [sz];
+
+% GA Options [epsilon float/binar display]
+gaOpts=[1e-6 1 1];
+
+% Generate an intialize population of size 20
+startPop = initializeoga(80,bounds,'tspEval',[1e-6 1]);
+
+% Lets run the GA
+%Hit a return to continue
+pause
+[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
+    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);
+
+% x is the best solution found
+x
+%Hit a return to continue
+pause
+
+% endPop is the ending population
+%endPop
+%%Hit a return to continue
+%pause
+
+% bestPop is the best solution tracked over generations
+bestPop
+%Hit a return to continue
+pause
+
+% trace is a trace of the best value and average value of generations
+trace
+
+%Hit a return to continue
+pause
+
+% Plot the best over time
+clf
+plot(trace(:,1),trace(:,2));
+%Hit a return to continue
+pause
+
+% Add the average to the graph
+hold on
+plot(trace(:,1),trace(:,3));
+%Hit a return to continue
+pause
+ 
+figure(2)
+clf
+A=ones(sz,sz);
+A= xor(triu(A),tril(A));
+[xg yg]=gplot(A,t);
+clf
+plot(xg,yg,'b:','MarkerSize',24);
+h=gca;
+hold on
+ap=x;
+plot(t(x(1:sz),1),t(x(1:(sz)),2),'r-') 
+plot(t([x(1),x(sz)],1),t([x(1),x(sz)],2),'r-') 
+plot(xg,yg,'b.','MarkerSize',24);
+
+j=1;
+for i=1:sz
+   str=sprintf('C-%d',j);
+   if t(i,1)<50
+     j=j+1;
+     text(t(i,1)-7,t(i,2),str);
+   else
+     j=j+1;
+     text(t(i,1)+2,t(i,2),str);
+   end
+ end
+ legend('Path','Best Found Path')
+echo off

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/orderbasedXover.m

@@ -1,3 +1,42 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:69ba737ee3db67e5ccae7c6f4a83ef02d1e44404b869d91e30ff7d07f91a010f
-size 1741
+function [c1,c2]= oox(p1,p2,bounds,Ops)
+% Orderbased crossover takes two parents P1,P2 and performs  order
+% based crossover by Davis.  
+%
+% function [c1,c2] = orderbasedXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+sz=size(p1,2)-1;
+n=floor(sz/2);
+cut1 = round(rand*(n-1)+0.5); %Generate random cut point U(1,n/2);
+cut2 = round(rand*(sz-cut1-1)+1+cut1); %Generate random cut point U(cut1+1,n-1);
+pm1=p1(1:end-1);
+pm2=p2(1:end-1);
+c1=p1;
+c2=p2;
+for i=[1:cut1 (cut2+1):sz]
+  pm1=strrep(pm1,p2(i),-1);
+  pm2=strrep(pm2,p1(i),-1);
+end
+c1((cut1+1):cut2)=p2(find(pm2>0));
+c2((cut1+1):cut2)=p1(find(pm1>0));

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/parse.m

@@ -1,3 +1,42 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4cac9baa65366e9604e3bf061005c8c9c73254320accbad0508aab2e733e3330
-size 1459
+function [x] = parse(inStr)
+% parse is a function which takes in a string vector of blank separated text
+% and parses out the individual string items into a n item matrix, one row
+% for each string.
+%
+% function [x] = parse(inStr)
+% x     - the return matrix of strings
+% inStr - the blank separated string vector
+
+% 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.
+
+sz=size(inStr);
+strLen=sz(2);
+x=blanks(strLen);
+wordCount=1;
+last=0;
+for i=1:strLen,
+  if inStr(i) == ' '
+    wordCount = wordCount + 1;
+    x(wordCount,:)=blanks(strLen);
+    last=i;
+  else
+    x(wordCount,i-last)=inStr(i);
+  end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/partmapXover.m

@@ -1,3 +1,53 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:178834d29e099dda975d1fb5e44ff726617dbe7b321a655077393f0215f85643
-size 1921
+function [ch1,ch2] = partmapXover(par1,par2,bounds,Ops)
+% Partmap crossover takes two parents P1,P2 and performs a partially
+% mapped crossover.  
+%
+% function [c1,c2] = partmapXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+sz = size(par1,2)-1;
+pos1 = round(rand*sz + 0.5);
+pos2 = round(rand*sz + 0.5);
+while pos2 == pos1
+   pos2 = round(rand*sz + 0.5);
+end
+if pos1 > pos2
+   t = pos1; pos1 = pos2; pos2 = t;
+end    
+ss1 = par1(pos1:pos2); ss2 = par2(pos1:pos2);
+ch1 = par2; ch2 = par1;
+for i = [1:pos1-1 pos2+1:sz]
+   ch1(i) = par1(i);
+   j = find(ch1(i) == ss2);
+   while ~isempty(j)
+      ch1(i) = ss1(j);
+      j = find(ch1(i) == ss2);
+   end
+   ch2(i) = par2(i);
+   j = find(ch2(i) == ss1);
+   while ~isempty(j)
+      ch2(i) = ss2(j);
+      j = find(ch2(i) == ss1);
+   end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/plotCorana.m

@@ -1,3 +1,26 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58a0548cb8f709d421a863c970d445fd28a949ffdd9ed4352de52a03c5a7d13d
-size 514
+function [z, a] = coranaEval(per)
+i=0;
+a=-0.9:per:0.9;
+sz=size(a,2);
+z=zeros(sz,sz);
+for x=a
+  i=i+1; j=0;
+  for y=a
+    j=j+1;
+    z(i,j)=coranaFeval([x y]);
+  end
+end
+%Done!
+
+%First let's look at it in each dimension independently
+clg
+plot(z(:,1)) %Plot a slice of the function in x max 250.25
+%Notice the range is [250.0-250.25]
+pause %Strike any key to continue
+clg
+plot(z(1,:)) %Plot a slice of the function in y 
+%Notice the range is [0-250]
+pause %Strike any key to continue
+mesh(a,a,z);
+view(30,60);
+grid;

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/roulette.m

@@ -1,3 +1,30 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5d1ca5ef24855b92e2008019100f13f5d742a4d1ca80e3ceb09a13d23c4b5a82
-size 861
+function[newPop] = roulette(oldPop,options)
+%roulette is the traditional selection function with the probability of
+%surviving equal to the fittness of i / sum of the fittness of all individuals
+%
+%function[newPop] = roulette(oldPop,options)
+%newPop  - the new population selected from the oldPop
+%oldPop  - the current population
+%options - options [gen]
+
+%Get the parameters of the population
+numVars = size(oldPop,2);
+numSols = size(oldPop,1);
+
+%Generate the relative probabilites of selection
+totalFit = sum(oldPop(:,numVars));
+prob=oldPop(:,numVars) / totalFit; 
+prob=cumsum(prob);
+
+rNums=sort(rand(numSols,1)); 		%Generate random numbers
+
+%Select individuals from the oldPop to the new
+fitIn=1;newIn=1;
+while newIn<=numSols
+  if(rNums(newIn)<prob(fitIn))
+    newPop(newIn,:) = oldPop(fitIn,:);
+    newIn = newIn+1;
+  else
+    fitIn = fitIn + 1;
+  end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/shiftMutation.m

@@ -1,3 +1,41 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9e6b2c4b6cb944a65676e5dc5fd2e71b76a74031793ef49b78c02f0ecda3f32b
-size 1624
+function [child] = shiftMutation(par,bounds,genInfo,Ops)
+% Shift mutation performs displaces one random gene
+% in a permutation to another position.
+%
+% function [newSol] =  shiftMutation(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for binaryMutation [gen prob_of_mutation]
+
+% 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.
+sz = size(par,2)-1;
+ppos = round(rand*sz + 0.5);
+cpos = round(rand*sz + 0.5);
+if ppos ~= cpos
+   if ppos > cpos
+      child = [par(1:cpos-1) par(ppos) par(cpos:ppos-1) par(ppos+1:sz) par(sz+1)];
+   else
+      child = [par(1:ppos-1) par(ppos+1:cpos) par(ppos) par(cpos+1:sz) par(sz+1)];
+   end
+else
+   child = par;
+end
+    
+   

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/simpleXover.m

@@ -1,3 +1,35 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8e1a8f55408647cb0302a09462db170ecfe93b45aa63784be6d5fe0aa9a5700f
-size 1611
+function [c1,c2] = simpleXover(p1,p2,bounds,Ops)
+% Simple crossover takes two parents P1,P2 and performs simple single point
+% crossover.  
+%
+% function [c1,c2] = simpleXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+
+numVar = size(p1,2)-1; 			% Get the number of variables 
+% Pick a cut point randomly from 1-number of vars
+cPoint = round(rand * (numVar-2)) + 1;
+
+c1 = [p1(1:cPoint) p2(cPoint+1:numVar+1)]; % Create the children
+c2 = [p2(1:cPoint) p1(cPoint+1:numVar+1)];

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/singleptXover.m

@@ -1,3 +1,39 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8c991b2e0baf8e5d73ec5ebc25daec74fc69d77559f205a476e369d4e91d6d70
-size 1564
+function [c1,c2]= singlePtX(p1,p2,bounds,Ops)
+% function [c1,c2] = singlePtXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+sz=size(p1,2)-1;
+cut = round(rand*(sz-1)+0.5); %Generate random cut point U(1,n-1)
+pm1=p1(1:sz);
+pm2=p2(1:sz);
+c1=p1;
+c2=p2;
+c1(1:cut)=p1(1:cut);
+c2(1:cut)=p2(1:cut);
+for i=1:cut
+  pm1=strrep(pm1,p2(i),-1);
+  pm2=strrep(pm2,p1(i),-1);
+end
+c1((cut+1):sz)=p2(find(pm2>0));
+c2((cut+1):sz)=p1(find(pm1>0));

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/startup.m

@@ -1,3 +1,2 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7a31db6b29276c6ca2dc034bb836520ca19dbf905b4af001a50177a412757e25
-size 49
+path(path,'/afs/eos/info/ie/ie589k_info/GAOT');
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/swapMutation.m

@@ -1,3 +1,40 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5b7bfb692a7ddaf40bdcce54c6d7fc49b00b1f3cd8f7345bbf73fadc56153ad5
-size 1569
+function [child] = swapmutate(par,bounds,genInfo,Ops)
+% Swap mutation exchanges the positions of two randomly
+% chosen genes in a permutation
+%
+% function [newSol] = adjswapmutation(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for binaryMutation [gen prob_of_mutation]
+
+% 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.
+sz = size(par,2)-1;
+pos1 = round(rand*sz + 0.5);
+pos2 = round(rand*sz + 0.5);
+if pos1 ~= pos2
+   if pos1 > pos2
+      t = pos1; pos1 = pos2; pos2 = t;
+   end
+   child = [par(1:pos1-1) par(pos2) par(pos1+1:pos2-1) par(pos1) par(pos2+1:(sz+1))];
+else
+   child = par;
+end
+    
+   

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/threeswapMutation.m

@@ -1,3 +1,36 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fe7ee1731fcf3603ad649fc3e7056517020950a8229d007f28ee0074a60f51da
-size 1397
+function [c] = threeswapMutation(p,bounds,genInfo,Ops)
+% Three swap mutation performs a three way swap of three randomly
+% chosen genes in a permutation
+%
+% function [newSol] = threeswapMutation(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for binaryMutation [gen prob_of_mutation]
+
+% 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.
+sz = size(p,2)-1;
+j=randperm(sz);
+jobs=j(1:3);
+c=p;
+c(jobs)=c(jobs(randperm(3)));
+
+
+    
+   

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/tournSelect.m

@@ -1,3 +1,36 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b113268e09db1dff4a918abc579b177f300ca904954d5132efc9fdab47c57edc
-size 1616
+function[newPop] = tournSelect(oldPop,options)
+% Performs a tournament selection.
+%
+% function[newPop] = tournSelect(oldPop,options)
+% newPop  - the new population selected from the oldPop
+% oldPop  - the current population
+% options - options to normGeomSelect [gen tournament_size]
+
+% 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.
+
+tournSize=options(2); 			% Get the number of tournaments
+e = size(oldPop,2); 			% xZome length
+n = size(oldPop,1); 			% number in Population
+newPop = zeros(n,e); 			% Create the memory for newPop
+tourns=floor(rand(tournSize,n)*n)+1; 	% Schedule of tournaments
+% Determine the winner of the tournaments
+[c b]=max(reshape(oldPop(tourns,e),tournSize,n));
+newPop=oldPop(diag(tourns(b,:)),:); 	% Copy the winners in to newPop
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/tspEval.m

@@ -1,3 +1,7 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e3b386c55aa70024d7c70026b0911c69506fdb64604a87cca12d3f94b265201c
-size 157
+function [sol,val] = tsp(sol,options)
+global distMatrix;
+sol;
+numvars=size(sol,2)-1;
+val = -sum(diag(distMatrix(sol(1:numvars),[sol(2:numvars) sol(1)])));
+
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/unifMutation.m

@@ -1,3 +1,34 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3ca545491a6958c1d656d6cdeb72b1ac1dce8f79cf6e99db8fc1a0c7ae90d536
-size 1648
+function [parent] = uniformMutate(parent,bounds,Ops)
+% Uniform mutation changes one of the parameters of the parent
+% based on a uniform probability distribution.
+%
+% function [newSol] = multiNonUnifMutate(parent,bounds,Ops)
+% parent  - the first parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options for uniformMutation [gen #UnifMutations]
+
+% 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.
+
+df = bounds(:,2) - bounds(:,1); 	% Range of the variables
+numVar = size(parent,2)-1; 		% Get the number of variables 
+% Pick a variable to mutate randomly from 1-number of vars
+mPoint = round(rand * (numVar-1)) + 1;
+newValue = bounds(mPoint,1)+rand * df(mPoint); % Now mutate that point
+parent(mPoint) = newValue; 		% Make the child

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/工具箱/遗传算法工具箱2/遗传工具箱/uniformXover.m

@@ -1,3 +1,55 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c27145aa2615b9bcaf5b21c1cc69d90a265ac44e104e4c0d0353b67c27e7b455
-size 1823
+function [ch1,ch2,t] = uniformxover(par1,par2,bounds,Ops)
+% Uniform crossover takes two parents P1,P2 and performs uniform
+% crossover on a permuation string.  
+%
+% function [c1,c2] = linearOrderXover(p1,p2,bounds,Ops)
+% p1      - the first parent ( [solution string function value] )
+% p2      - the second parent ( [solution string function value] )
+% bounds  - the bounds matrix for the solution space
+% Ops     - Options matrix for simple crossover [gen #SimpXovers].
+
+% 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.
+sz = size(par1,2)-1;
+ch1 = par1;
+ch2 = par2;
+t = round(rand(1,sz));
+szt = sum(t);
+idx = 1:sz;
+idxt = idx(logical(t));
+plst = idxt;
+for i = 1:szt
+   plst(i) = find(par2 == par1(idxt(i)));
+end
+plst = sort(plst);
+for i = 1:szt
+   ch1(idxt(i)) = par2(plst(i));
+end
+szt = sz - szt;
+idxt = idx(~t);
+plst = idxt;
+for i = 1:szt
+   plst(i) = find(par1 == par2(idxt(i)));
+end
+plst = sort(plst);
+for i = 1:szt
+   ch2(idxt(i)) = par1(plst(i));
+end
+ 
+   

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/B2F.m

@@ -1,3 +1,22 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:caea6aa308c917bc76e4d3980872e51b48889a0d128b746fe638d85f752e5ef7
-size 658
+function  [B,len,v]=B2F(sol,bounds)
+%[B,len]=B2F(x,bounds)    �����Ʊ��뺯��
+%x                        ����������x=[6 8 9];
+%bounds                   �߽�Լ��ru��bounds=[4 8 ;3  11;6  12;];
+%B                        �����Ʊ��봮
+%���볤��L��bounds(2)-bounds(1)����
+%������:
+%     ���볤������L=[4 8 6]��ɶ�����L=[11 1000 110],��len=[2 4 3]
+%     ����B=x-bound(1)=[2 5 3]��ɶ����� B=[10 0101 011]
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+n=length(sol);
+len=[];B=[];v=[];
+L=bounds(:,2)-bounds(:,1);
+L=de2bi(L);
+for i=1:n
+len(i)=length(L(i,:));
+end
+v=sol-bounds(:,1)';
+for i=1:n
+    B=[B de2bi(v(i),len(i))];
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/F2B.m

@@ -1,3 +1,29 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a654ccb2ce5b7741bacf18e5a16ae600391206a70d4f5bb6daebdbb2ed3030ef
-size 540
+function  [pops,len]=F2B(x,bounds,len)
+%�����Ʊ���ת��Ϊʮ����
+%[pops]=F2B(x,bounds,len)
+%x       �����ƴ���x=[0 1 1  0 0 1 0  1 0 1 0 1]
+%len     �����ƴ��ķֶ�len=[3  4  5]
+%bounds  �߽�Լ��
+%pops    ʮ����
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+n=length(x);
+m=length(len);
+q=[];
+for i=1:m
+q(i)=sum(len(1:i));
+end
+q=[0 q];
+for j=1:m
+    pops(j)=bounds(j,1);
+    p=[];
+    p=x(q(j)+1:q(j+1));
+    L1=q(j+1)-q(j);
+    for k=1:L1
+        pops(j)=pops(j)+p(k)*2^(k-1);
+    end
+end
+
+    
+
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/INTinti.m

@@ -1,3 +1,16 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:86e35ec011f252b6ca717adb34969009a3ebc0aceb4c9c4c24b22724a000e949
-size 375
+function [pop]=INTinti(num,bounds)
+%[pop]=INTinti(num,bounds)
+%inti     ���뺯��
+%num      ��Ⱥ��
+%bounds   �߽�Լ��
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+n=size(bounds,1);
+L=bounds(:,2)-bounds(:,1);
+p=rand(num,n);
+for i=1:num
+    p(i,:)=round(p(i,:).*L');
+    pop(i,:)= p(i,:)+bounds(:,1)';
+    f(i)=myfun(pop(i,:));
+end
+pop=[pop f'];

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/changes.m

@@ -1,3 +1,49 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6131cf2e58e7b2cd7e9544b4bf335f9125fc89508b6b115d574e43c2a6e1a1d3
-size 959
+function [pops]=changes(cpop,bounds,len,p)
+%����ͻ�亯��
+%function [pops]=changes(pop,bounds,len,p)
+%pop        ��Ⱥ��Ŀ
+%bounds     �߽�Լ��
+%len        ÿ�������ı��볤��
+%           ��lenΪ[4 3 3];��ʾ��������������һ�������Ķ����Ʊ��볤��Ϊ4,��������
+%p          ͻ�����
+%pops       ����ͻ���Ļ���
+%p1         ����ͻ����Ŀ
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+if isempty(p)
+    p=0.01;
+end
+[n,m]=size(cpop);
+pop=cpop;
+p1=round(sum(len)*n*p);
+k=0;q=[];v=[];
+while(k<p1)
+    k=k+1;
+    q(k)=round(rand*(sum(len)*n-1))+1;
+    for i=1:k-1
+        if q(k)==q(i)
+            q(k)=[];
+            k=k-1;
+        end
+    end
+end
+
+for i=1:n
+[B(i,:),len]=B2F(pop(i,:),bounds);
+end
+
+v=reshape(B,1,n*sum(len));
+
+for i=1:p1
+    if v(q(i))==0
+        v(q(i))=1;
+    else
+        v(q(i))=0;
+    end
+end
+v=reshape(v,n,sum(len));
+for i=1:n
+   pop(i,:)=F2B(v(i,:),bounds,len);
+end
+pops=pop
+cpop

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/cross.m

@@ -1,3 +1,57 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8eddbc1746d3ce06c04e4f33f27630936f1363ff0bd4e46b9e4728f0d6a4ef96
-size 1155
+function  [cpop ,len,v]=cross(child,bounds,CP)
+%���溯������ȡ�㽻��
+%[newpop ,len]=cross(child,bounds,CP)
+%child      ���ƺ����Ⱥ
+%bounds     �߽�Լ��
+%CP         �������
+%newpop     ����������Ⱥ
+%len        ÿ�������ı��볤��
+%           ��len����Ϊ[4 3 3];��ʾ��������������һ�������Ķ����Ʊ��볤��Ϊ4,��������
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+if isempty(CP)
+    CP=0.25;
+end
+[n ,m]=size(child);
+B=[];len=[];t=[];
+mychild=child(:,1:end-1);
+v=[];
+p=rand(1,n);
+k=1;
+    for i=1:n
+        if p(i)<CP
+            v(k)=i;
+            k=k+1;
+        end
+    end
+    if (rem(k,2)==0)
+        temp=v(k-1);
+        while (temp==v(k-1))
+        temp=round(rand*(n-1))+1;
+    end
+    v(k)=temp;
+    k=k+1;
+   end 
+if isempty(v)
+    [B(i,:),len]=B2F(mychild(1,:),bounds);
+    B=[];
+else
+    for i=1:k-1
+[B(i,:),len]=B2F(mychild(v(i),:),bounds);
+end
+for i=1:2:k-2
+    p2=round(rand*sum(len)-1)+1;
+    t=zeros(1,p2);
+    t(i,:)=B(i,1:p2);
+    B(i,1:p2)=B(i+1,1:p2);
+    B(i+1,1:p2)=t(i,:);
+end
+for i=1:k-1
+   mychild(v(i),:)=F2B(B(i,:),bounds,len);
+end
+end
+cpop=mychild;
+
+
+
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/de2bi.m

@@ -1,3 +1,34 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4f30725723950ae1bdcef724606abbee72b698362ef807e41355418a50fe37e9
-size 1048
+function b = de2bi(d, n, p)
+%function b = de2bi(d, n, p)
+%DE2BI  ת��10������Ϊ����������
+%        B = DE2BI(D) ת������������D�ɶ����ƾ���B��
+%        �����ƾ���B��ÿһ�б�ʾʮ��������D����Ӧ������
+%       B = DE2BI(D, N) ת������������D�ɶ����ƾ���B��
+%        ��ָ��B������ΪN��
+%       B = DE2BI(D, N, P) ת������������D��p���ƾ���B��
+%      p���ƾ���B��ÿһ�б�ʾʮ��������D����Ӧ������
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+d = d(:);len_d = length(d);
+if min(d) < 0, error('Cannot convert a negative number');
+elseif ~isempty(find(d==inf)),
+     error('Input must not be Inf.');
+elseif find(d ~= floor(d)), 
+    error('Input must be an integer.');  
+end;
+if nargin < 2,
+   tmp = max(d); b1 = [];
+   while tmp > 0
+      b1 = [b1 rem(tmp, 2)];tmp = floor(tmp/2);
+   end;
+   n = length(b1);
+end;
+if nargin < 3,p = 2;end;
+b = zeros(len_d, n);
+for i = 1 : len_d
+   j = 1;tmp = d(i);
+   while (j <= n) & (tmp > 0)
+      b(i, j) = rem(tmp, p);tmp = floor(tmp/p);
+      j = j + 1;
+end;end;
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/f553.m

@@ -1,3 +1,27 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c3efd92dd04b3ec59c80ec94d94e6296dc2f327da97efc9df149e05e76ed1658
-size 538
+function [sol,eval]=f553(sol,options)
+m(1)=sol(1);
+m(2)=sol(2);
+m(3)=sol(3);
+%ʧЧ���ʾ���
+q=[0.01 0.05 0.10 0.18;
+0.08 0.02 0.15 0.12;
+0.04 0.05 0.20 0.10];
+%Լ������
+g1=51-(m(1)+3).^2+m(2).^2+m(3).^2;
+g2=20*sum(m+exp(-m))-120;
+g3=20*sum(m.*exp(-m/4))-65;
+%����ӳͷ������ֵ
+if ((g1>=0)&(g2>=0)&(g3>=0))
+  multi=1;
+  for i=1:3
+     summ=0;
+     for j=2:4
+        summ=summ+q(i,j).^(m(i)+1);
+     end      
+     multi=multi*(1-(1-(1-q(i,1)).^(m(i)+1))-summ);
+  end
+  eval=multi;
+else
+%ȡM=500
+  eval=-500;
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/ga.m

@@ -1,3 +1,47 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63c2ae74e4a035bbec682e2c8ef9d6b7cb3481ecd2713f56cc86dc7b6e5e8f37
-size 1069
+function [f,x]=myga(num,bounds,Myfun,N,CP,P)
+%[f,x]=ga(num,bounds,fun,N,CP,P)
+%���Ŵ��㷨�����ڣ�
+%           Ŀ�꺯��Ϊ�����ֵ���ҽ�Ǹ�������
+%bounds     �߽�Լ��
+%Myfun        ΪĿ�꺯��
+%num        ��ʼ��Ⱥ��
+%N          ����������
+%CP         �������
+%P          ͻ�����
+%f          Ŀ�����Ž�
+%x          ���Ž�����
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+m=nargin;
+if m<6
+disp('-_-  ����!')
+disp('>> �������̫��')
+disp('>>  ���س����鿴����')
+    pause
+    help ga
+    f='-_- ';
+    x='û�й�ز��ɷ�Բ';
+    break;
+end
+pop=INTinti(num,bounds);
+fmax=pop(:,end);
+endpop=pop;
+n=size(endpop,2);
+k=0;x=[];f=zeros(1,num);
+while(k<N)
+    pop=mutation(endpop);
+    [cpop ,len,v]=cross(pop,bounds,CP);
+    [pops]=changes(cpop,bounds,len,P);break;
+    for i=1:num
+        sol=pops(i,:);
+        [f(i)]=Myfun(sol);
+        if fmax(i)<f(i)
+            fmax(i)=f(i);
+            endpop(i,1:end-1)=pops(i,:);
+        end
+end
+endpop(:,end)=fmax(:);
+k=k+1;
+end
+[f,ii]=max(fmax);
+x=endpop(ii,1:end-1);

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/gaDemo1Eeval.m

@@ -1,3 +1,8 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:82092f0e0f6cd34f61a92150513e4c7826f922afcb9cd933ad587ca2cac7106e
-size 172
+function [sol, eval] =gaDemo1Eeval(sol,options)
+x=sol(1);
+eval = x + 10*sin(5*x)+7*cos(4*x);
+
+%����˵��
+%eval���������Ӧ�ȣ� 
+%sol����ǰ���壬n+1��Ԫ�ص���������
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/mutation.m

@@ -1,3 +1,29 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8b1dcac14752d20cfa8a63de5d92b869f5efd00cb7650bbb9cce458a603a390a
-size 529
+function [child]=mutation(pop)
+%���ƺ�������ȡС����ת��
+%[child]=mutation(pop)
+%mutation    ����
+%pop         ��ʼ��Ⱥ
+%child       ���ظ��ƺ����Ⱥ
+%pop(:,end)  ��ֵ��
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+[n,m]=size(pop);
+f=pop(:,end);
+value=sum(f);
+for i=1:n
+    p(i)=f(i)/value;
+    q(i)=sum(p(1:i));
+end
+    t=rand(1,n);
+for j=1:n
+    for k=1:n
+        if t(j)<q(k)
+            v(j)=k;
+            break
+        end
+    end
+end
+    i=1:n;
+    child(i,:)=pop(v(i),:);
+
+

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/myfun.m

@@ -1,3 +1,9 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b1855695ae6d222117e6b1937ef2063bbcdbb87810659120e19177ed1da2ed9c
-size 153
+function [f]=myfun(sol,bnd)
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+x=sol;
+n=length(x);
+f=0;
+for i=1:n
+    f=f+x(i)*i;
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/myga.m

@@ -1,3 +1,81 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e81f0d48d2000673960cd2f40e86e892a8cab20282d90de7bc0ae7280a0552b2
-size 1608
+function [f,x]=myga(num,bounds,N,CP,P)
+%[f,x]=ga(num,bounds,fun,N,CP,P) 
+%[f,x]=myga([],bounds,[],[],[])
+%���Ŵ��㷨�����ڣ�
+%           Ŀ�꺯��Ϊ�����ֵ���ҽ�Ǹ�������
+%bounds     �߽�Լ��
+%Myfun      ΪĿ�꺯��
+%num        ��ʼ��Ⱥ��
+%N          ����������
+%CP         �������
+%P          ͻ�����
+%f          Ŀ�����Ž�
+%x          ���Ž�����
+%           ���ߣ�����01-2�����º�
+%           zxh21st@163.com
+m=nargin;
+if m<5
+disp('-_-  ����!')
+disp('>> �������̫��')
+disp('>>  ���س����鿴����')
+    pause
+    help ga
+    f='-_- ';
+    x='û�й�ز��ɷ�Բ';
+    break;
+end
+if isempty(CP)
+    CP=0.25;
+end
+if isempty(P)
+    P=0.01;
+end
+if isempty(N)
+    N=1000;
+end
+if any(bounds(:,1))<0
+    disp('-_-  ����!')
+disp('>>  ���س����鿴����')
+    pause
+    help ga
+    f='-_- ';
+    x='û�й�ز��ɷ�Բ';
+    break;
+end
+if isempty(num)
+    num=100;
+end
+pop=INTinti(num,bounds);
+fmax=pop(:,end);
+endpop=pop;
+n=size(endpop,2);
+count=0;x=[];f=zeros(1,num);
+while(count<N)
+    pop=mutation(endpop);
+    [cpop ,len,v]=cross(pop,bounds,CP);
+    [pops]=changes(cpop,bounds,len,P);
+    for i=1:num
+        sol=pops(i,:);
+    [f(i)]=Myfun(sol);
+    %�ͷ�����
+for jj=1:length(sol)
+    if sol(jj)<bounds(jj,1)
+        f(i)=-inf;
+    end
+    if sol(jj)>bounds(jj,2)
+        f(i)=-inf;
+    end
+end
+            if fmax(i)<f(i)
+            fmax(i)=f(i);
+            endpop(i,1:end-1)=pops(i,:);
+        end
+end
+endpop(:,end)=fmax(:);
+count=count+1;
+
+% [f,ii]=max(fmax);
+% x=endpop(ii,1:end-1);
+end
+[f,ii]=max(fmax);
+x=endpop(ii,1:end-1);

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/用MATLAB实现遗传算法程序/xcross.m

@@ -1,3 +1,4 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2ec2484033d8e1167922fbb2b1b9f7c57f2c16034f64f52652be91c9cd4cec84
-size 131
+function  [newpop ,len]=xcross(child,bounds,CP)
+ mychild=child(:,1:end-1);
+[B(1,:),len]=B2F(mychild(1,:),bounds);
+newpop=B(1,:);

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/遗传算法的一些应用于编程（含源代码）/MTSP问题求解/MTSP的求解程序代码/ceshide.m

@@ -1,3 +1,4 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a340ffcbf05f99980a5b09aad080efa8eca418f5100c063929276a17ca6b76de
-size 232
+clear all;clc;close all;
+load lujing;                             %���д洢ͼ���ڽӾ�����������Ϊ���
+dmat=floyed(tu);                         %��ͼ����Ӧ�����·������
+[x1,x2,x3,x4]=mtspf_ga(dmat,3,16,100);   %����������Ž⡣

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/遗传算法的一些应用于编程（含源代码）/MTSP问题求解/MTSP的求解程序代码/floyed.m

@@ -1,3 +1,37 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ccb198e8f01bcb03064ed5f427c69a8468a5a49611dc2f116d8448c5617c9b83
-size 927
+function  matrix=floyed(G)
+%�洢����ͼ���ڽӾ��� 
+%{
+G=[ inf inf 10 inf 30 100;
+    inf inf 5  inf inf inf;
+    inf 5 inf 50 inf inf;
+    inf inf inf inf inf 10;
+    inf inf inf 20 inf 60;
+    inf inf inf inf inf inf;];
+%}
+d(1,:,:)=G;
+%������һ�����һ�� ��Ӧ���ʱ�������Ż��ľ���
+for i=1:size(G,1)
+    for j=1:size(G,2)
+        s(i,j).trace=i;
+        if d(1,i,j)<=d(1,i,1)+d(1,1,j)
+           d(1,i,j)=d(1,i,j);
+        else
+           d(1,i,j)=d(1,i,1)+d(1,1,j);
+        end
+    end
+end
+%�����ӵڶ� �� ������� ��ʱ�� ·���Ż�
+for k=2:size(G,1)
+    for i=1:size(G,1)
+        for j=1:size(G,1)
+            if d(k-1,i,j)<=d(k-1,i,k)+d(k-1,k,j)
+                d(k,i,j)=d(k-1,i,j);
+            else
+                d(k,i,j)=d(k-1,i,k)+d(k-1,k,j);
+            end
+        end
+    end
+end
+matrix=zeros(size(G,1),size(G,1));
+matrix=d(size(G,1),:,:);
+matrix=reshape(matrix,size(G,1),size(G,1));

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/遗传算法的一些应用于编程（含源代码）/MTSP问题求解/MTSP的求解程序代码/mtspf_ga.m

@@ -1,3 +1,184 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8abadf1a9de5ec2d001933d0cba8d15089bb3e4dd7fc9b664dc9c67ff3759315
-size 6108
+function varargout = mtspf_ga(dmat,salesmen,min_tour,pop_size,num_iter,show_prog,show_res)
+%dmat ���������м�����·������ͨ��floyed�㷨��ý����
+%salesmen �����̸���
+%min_tour ÿ�����������ٷ��ʵij�����
+%pop_size ��Ⱥ������
+%num_iter  �����Ĵ���
+%show_prog,show_res ��ʾ�IJ����趨
+nargs = 7;                      %���������������������һЩĬ�ϵIJ�����
+for k = nargin:nargs-1
+    switch k
+        case 0
+            dmat = 10*rand(20,20);
+        case 1
+            salesmen = 5;
+        case 2
+            min_tour = 2;
+        case 3
+            pop_size = 80;
+        case 4
+            num_iter = 5e3;
+        case 5
+            show_prog = 1;
+        case 6
+            show_res = 1;
+        otherwise
+    end
+end
+
+% ������� ����
+[nr,nc] = size(dmat);
+if nr ~= nc
+    error('Invalid XY or DMAT inputs!')
+end
+n = nr - 1; % ��ȥ��ʼ�ij��к�ʣ��ij��е���
+
+% ��������ļ��
+salesmen = max(1,min(n,round(real(salesmen(1)))));
+min_tour = max(1,min(floor(n/salesmen),round(real(min_tour(1)))));
+pop_size = max(8,8*ceil(pop_size(1)/8));
+num_iter = max(1,round(real(num_iter(1))));
+show_prog = logical(show_prog(1));
+show_res = logical(show_res(1));
+
+% ��ʼ��·�ߡ��ϵ��ѡ��
+num_brks = salesmen-1;
+dof = n - min_tour*salesmen;          % �������ɷ��ʵij�����
+addto = ones(1,dof+1);
+for k = 2:num_brks
+    addto = cumsum(addto);
+end
+cum_prob = cumsum(addto)/sum(addto);
+
+% ��ʼ����Ⱥ
+pop_rte = zeros(pop_size,n);          % ·�����ϵ���Ⱥ
+pop_brk = zeros(pop_size,num_brks);   % �ϵ㼯�ϵ���Ⱥ
+for k = 1:pop_size
+    pop_rte(k,:) = randperm(n)+1;
+    pop_brk(k,:) = randbreaks();
+end
+
+% ѡ���ͼʱ�ĸ����˵���ɫ  ��ɾȥ��
+clr = [1 0 0; 0 0 1; 0.67 0 1; 0 1 0; 1 0.5 0];
+if salesmen > 5
+    clr = hsv(salesmen);
+end
+
+% ��ʼ�����Ŵ��㷨����
+global_min = Inf;                %��ʼ�����·��
+total_dist = zeros(1,pop_size);
+dist_history = zeros(1,num_iter);
+tmp_pop_rte = zeros(8,n);        %��ǰ��·������
+tmp_pop_brk = zeros(8,num_brks); %��ǰ�Ķϵ�����
+new_pop_rte = zeros(pop_size,n); %���µ�·������
+new_pop_brk = zeros(pop_size,num_brks);%���µĶϵ�����
+if show_prog
+    pfig = figure('Name','MTSPF_GA | Current Best Solution','Numbertitle','off');
+end
+for iter = 1:num_iter
+    % ����ÿһ������Ⱥ ��Ӧ���������ѡ��
+    for p = 1:pop_size
+        d = 0;
+        p_rte = pop_rte(p,:);
+        p_brk = pop_brk(p,:);
+        rng = [[1 p_brk+1];[p_brk n]]';
+        for s = 1:salesmen
+            
+            d = d + dmat(1,p_rte(rng(s,1))); % ���ӿ�ʼ��·��
+            for k = rng(s,1):rng(s,2)-1
+                d = d + dmat(p_rte(k),p_rte(k+1));
+            end
+            d = d + dmat(p_rte(rng(s,2)),1); % ���ӽ����ĵ�·��
+            dis(p,s)=d;
+            %d=d+myLength(dmat,p_rte(rng(s,1):rng(s,2)));%�ɵ��ú�������
+        end
+        total_dist(p) = d;
+        %distan(p)=max(dis(p,:));%�����������е����ֵ
+    end
+    
+    % ��ÿ����Ⱥ���ҵ���õ�·��
+    [min_dist,index] = min(total_dist);
+    dist_history(iter) = min_dist;     %+max(distan);
+
+    if min_dist < global_min
+        global_min = min_dist;
+        opt_rte = pop_rte(index,:);             %���ŵ����·��
+        opt_brk = pop_brk(index,:);             %���ŵĶϵ�����
+        rng = [[1 opt_brk+1];[opt_brk n]]';     %���ü�¼�ϵ�ķ���
+    end
+
+    % �Ŵ��㷨���ӵIJ�������
+    rand_grouping = randperm(pop_size);
+    for p = 8:8:pop_size
+        rtes = pop_rte(rand_grouping(p-7:p),:);
+        brks = pop_brk(rand_grouping(p-7:p),:);
+        dists = total_dist(rand_grouping(p-7:p));
+        [ignore,idx] = min(dists);
+        best_of_8_rte = rtes(idx,:);
+        best_of_8_brk = brks(idx,:);
+        rte_ins_pts = sort(ceil(n*rand(1,2)));
+        I = rte_ins_pts(1);
+        J = rte_ins_pts(2);
+        for k = 1:8 % �����µķ���
+            tmp_pop_rte(k,:) = best_of_8_rte;
+            tmp_pop_brk(k,:) = best_of_8_brk;
+            switch k
+                case 2 % ����
+                    tmp_pop_rte(k,I:J) = fliplr(tmp_pop_rte(k,I:J));
+                case 3 % ��������
+                    tmp_pop_rte(k,[I J]) = tmp_pop_rte(k,[J I]);
+                case 4 % ����ƽ�Ʋ���
+                    tmp_pop_rte(k,I:J) = tmp_pop_rte(k,[I+1:J I]);
+                case 5 % ���¶ϵ�
+                    tmp_pop_brk(k,:) = randbreaks();
+                case 6 % ���ò����¶ϵ�
+                    tmp_pop_rte(k,I:J) = fliplr(tmp_pop_rte(k,I:J));
+                    tmp_pop_brk(k,:) = randbreaks();
+                case 7 % ���������¶ϵ�
+                    tmp_pop_rte(k,[I J]) = tmp_pop_rte(k,[J I]);
+                    tmp_pop_brk(k,:) = randbreaks();
+                case 8 % ���鲢���¶ϵ�
+                    tmp_pop_rte(k,I:J) = tmp_pop_rte(k,[I+1:J I]);
+                    tmp_pop_brk(k,:) = randbreaks();
+                otherwise % �������
+            end
+        end
+        new_pop_rte(p-7:p,:) = tmp_pop_rte;
+        new_pop_brk(p-7:p,:) = tmp_pop_brk;
+    end
+    pop_rte = new_pop_rte;
+    pop_brk = new_pop_brk;
+end
+
+% ���ؽ������
+rng = [[1 opt_brk+1];[opt_brk n]]';
+dis_e=zeros(1,salesmen);    %���ò�����ÿ�������̵����·��
+for s = 1:salesmen
+    dis_e(s)=myLength(dmat,opt_rte(rng(s,1):rng(s,2)));
+end
+
+if nargout
+    varargout{1} = opt_rte;
+    varargout{2} = opt_brk;
+    varargout{3} = min_dist;
+    varargout{4} = dis_e;
+end
+%�����������̵�ͼʾ
+plot(dist_history);
+grid on;xlabel('�����Ĵ���');ylabel('���ߵ�·��֮��');
+    % �������һ�׶ϵ� �ļ���
+    function breaks = randbreaks()
+        if min_tour == 1 % һ��������ʱ��û�жϵ������
+            tmp_brks = randperm(n-1);
+            breaks = sort(tmp_brks(1:num_brks));
+        else % ǿ�ƶϵ�����  �� ����̵����г���
+            num_adjust = find(rand < cum_prob,1)-1;
+            spaces = ceil(num_brks*rand(1,num_adjust));
+            adjust = zeros(1,num_brks);
+            for kk = 1:num_brks
+                adjust(kk) = sum(spaces == kk);
+            end
+            breaks = min_tour*(1:num_brks) + cumsum(adjust);
+        end
+    end
+end

matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/遗传算法的一些应用于编程（含源代码）/MTSP问题求解/MTSP的求解程序代码/myLength.m

@@ -1,3 +1,6 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b3387939f0f3355f00b9bb8e9714d114e26737bcbdba2f7e42d88c78d99de7b6
-size 123
+function len=myLength(D,p)
+N=length(p);
+len=D(p(1,N),1)+D(1,p(1,1));
+for i=1:(N-1)
+    len=len+D(p(1,i),p(1,i+1));
+end

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matlab数学建模算法/遗传算法@遗传退火算法（算法简介+编程技巧+工具箱+应用大全）（含源代码）/遗传算法的一些应用于编程（含源代码）/TSP问题求解/基于Matlab的遗传算法解决TSP问题的报告.txt

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+----------------------- Page 1-----------------------
+
+                         基于Matlab的遗传算法解决TSP问题 
+                         基于Matlab的遗传算法解决TSP问题 
+          报告题目：基基于于MMaattllaabb的的遗遗传传算算法法解解决决TTSSPP问问题题 
+
+说明： 
+说明： 
+说说明明：：该文包括了基于Matlab的遗传算法解决TSP问题的 
+
+基本说明，并在文后附录了实现该算法的所有源代码。此代 
+
+码经过本人的运行，没有发现错误，结果比较接近理论最优 
+
+值，虽然最优路径图有点交叉。 
+
+      因为本人才疏学浅，本报告及源代码的编译耗费了本人 
+
+较多的时间与精力，特收取下载积分，还请见谅。若有什么 
+
+问题，可以私信，我们共同探讨这一问题。 
+
+      希望能对需要这方面的知识的人有所帮助！ 
+
+----------------------- Page 2-----------------------
+
+1. 
+1.问题介绍 
+11.. 
+
+    旅行商问题(Traveling Salesman Problem，简称TSP)是一个经典的组合优 
+化问题。它可以描述为：一个商品推销员要去若干个城市推销商品，从一个城市 
+出发，需要经过所有城市后，回到出发地，应如何选择行进路线，以使总行程最 
+短。从图论的角度看，该问题实质是在一个带权完全无向图中。找一个权值最小 
+的Hemilton回路。其数学描述为：设有一个城市集合其中每对城市之间的距离 
+d c,c ∈R+ ，求一对经过C中每个城市一次的路线 c ,c ,⋯c  使 
+  ( i j)                                          ( Π1  Π2   Πn) 
+
+                              n−1 
+
+                          min  d c ,c       +d c ,c 
+                             ∑ ( Πi  Π i+1 )   ( Πn  Π ) 
+                                        ( )           1 
+                              i=1 
+
+    其中Π ,Π ,⋯Π 是1，2⋯n 的一个置换。 
+           1  2   n  (     ) 
+
+2. 
+2.遗传算法 
+22.. 
+
+2.1遗传算法基本原理 
+2.1遗传算法基本原理 
+22..11遗遗传传算算法法基基本本原原理理 
+
+    遗传算法是由美国J.Holland教授于1975年在他的专著《自然界和人工系 
+统的适应性》中首先提出的，它是一类借鉴生物界自然选择和自然遗传机制的随 
+机化搜索算法。 
+    遗传算法模拟自然选择和自然遗传过程中发生的繁殖、交叉和基因突变现 
+象，在每次迭代中都保留一组候选解，并按某种指标从解群中选取较优的个体， 
+利用遗传算子(选择、交叉和变异)对这些个体进行组合，产生新一代的候选解群， 
+重复此过程，直到满足某种收敛指标为止。 
+    遗传算法，在本质上是一种不依赖具体问题的直接搜索方法，是一种求解问 
+题的高效并行全局搜索方法。遗传算法在模式识别、神经网络、图像处理、机器 
+学习、工业优化控制、自适应控制、负载平衡、电磁系统设计、生物科学、社会 
+科学等方面都得到了应用。在人工智能研究中，现在人们认为“遗传算法、自适 
+应系统、细胞自动控制、混沌理论与人工智能一样，都是对今后十年的计算技术 
+有重大影响的关键技术”。 
+
+2.2遗传算法的流程 
+2.2遗传算法的流程 
+22..22遗遗传传算算法法的的流流程程 
+
+    标准的遗传算法包括群体的初始化，选择，交叉，变异操作。流程图如图1 
+所示，其主要步骤可描述如下： 
+    （1）随机产生一组初始个体构成的初始种群，并评价每一个个体的适配值。 
+    （2）判断算法的收敛准则是否满足。若满足输出搜索结果；否则执行以下 
+步骤。 
+
+----------------------- Page 3-----------------------
+
+     （3）根据适配值大小以一定方式执行选择操作。 
+     （4）按交叉概率Pc执行交叉操作。 
+     （5）按变异概率Pm执行变异操作。 
+       6             2 
+     （ ）返回步骤（ ）。 
+
+                            图 1  遗传算法流程图 
+
+3.TSP 
+3.TSP 
+33..TTSSPP问题的遗传算法设计与实现 
+
+3.1TSP 问题的图论描述 
+3.1TSP 问题的图论描述 
+33..11TTSSPP问问题题的的图图论论描描述述 
+
+     求最短路径问题，用图论术语描述如下：在图G(V,A)中，V表示顶点集合， 
+
+V=(v1,v2,…,vn)对G中的某一边(v,v )，相应的有一个数d(v,v )，如果G中不 
+                                 i  j                       i j 
+
+存在边(v,v )，则令d(v,v )无穷大，如果把d(v,v )认为是边(v,v )的长度， 
+         i  j           i  j                    i  j            i  j 
+
+则通路的长度定义为组成路的各条边的长度总和。顶点v,v 之间是否有边相连， 
+                                                       i  j 
+
+由邻接矩阵来决定。 
+
+----------------------- Page 4-----------------------
+
+              A               v         e          G             A=[  ] 
+    邻接矩阵 ：对一个具有 个顶点， 条边的图 的邻接矩阵                                      a 是一 
+                                                                       ij 
+
+   v×v                 =1                       =0      vi  vj 
+个      阶方阵，其中a  ，表示v和v 邻接，a                        表示 和 不相邻接（或 
+                      ij         i    j        ij 
+
+i=j）。 
+
+3.2读取txt 文件 
+3.2读取txt 文件 
+33..22读读取取ttxxtt文文件件 
+
+    标准的测试文件一般都是存储 n*2 的 txt 文件，为此本人编译了一个 
+readfile.m的程序，方便了不同文件的使用。此程序返回的是城市的坐标矩阵pop 
+和城市的距离矩阵dista。 
+
+3.3初始种群 
+3.3初始种群 
+33..33初初始始种种群群 
+
+    对于n个城市的问题，每个个体即每个解的长度为n ，用s行, t列的pop矩阵 
+表示初始群体，s表示初始群体的个数，t为n+1，矩阵的每一行的前n个元素表示 
+城市编码，最后一个元素表示这一路径的长度。这一算法通过start.m程序实现。 
+
+3.3适应度 
+3.3适应度 
+33..33适适应应度度 
+
+       TSP 
+    在  的求解中，可以直接用距离总和作为适应度函数。个体的路径长度越 
+                      pop 
+小，所得个体优越，以  矩阵的每一行最后一个元素作为个体适应值。求适应 
+值的qiujuli.m 程序，见附录。 
+
+3.4选择 
+3.4选择 
+33..44选选择择 
+
+    选择就是从群体中选择优胜个体、淘汰劣质个体的操作，它是建立在群体中 
+个体适应度评估基础上。这里采用方法是最优保存方法。 
+    算法就是首先将群体中适应度最大的k个个体直接替换适应度最小的k个个 
+体。程序为select.m，见附录。 
+
+3.5交 
+3.5交 
+33..55交交叉 
+
+    受贪婪算法的启发, 本文设计一种有目的使适应值上升的交叉算子。已知两 
+个父代a1(m11,m12,m13,...,m1n),a2(m21,m22,m23,...,m2n)，算法产生后代a1’ 
+和a2’的过程如下： 
+    (1) 随机产生一个城市d作为交叉起点, 把d作为a1’和a2’的起始点 
+    (2) 分别从a1和a2中找出d的右城市dr1和dr2,并计算(d,dr1)和(d,dr2)的 
+距离j1和j2。 
+    (3) 如果j1<j2,则把dr1作为a1'的第二个点，从a1和a2中删除d，并且把当 
+前点改为dr1.转步骤(5)。 
+
+----------------------- Page 5-----------------------
+
+     (4) 如果j1>j2,则把dr2作为a1'的第二个点，从a1和a2中删除d，并且把当 
+前点改为dr2。 
+     (5) 若此时p1和p2的个数为1，结束，否则回到第二步继续执行。 
+     同理，把第二步中的右城市改成左城市dle1和dle2，通过计算(d,d1e1)和 
+(d,d1e2) 的距离并比较大小来确定子代a2'。为程序cross.m，见附录。 
+
+3.6变异 
+3.6变异 
+33..66变变异异 
+
+     变异操作是以变异概率Pm对群体中个体串某些基因位上的基因值作变动，若 
+变异后子代的适应度值更加优异，则保留子代染色体，否则，仍保留父代染色体。 
+这里采用的方法是倒置变异法。 
+     假设当前个体X为（1 3 7 4 8 0 5 9 6 2）。如果Pm>rand，那么随机选择 
+来自同一个体的两个点mutatepoint(1)和mutatepoint(2)，比如说3和7，倒置P1 
+和P之间的部分，产生下面的子体X'为(1 3 7 5 0 8 4 9 6 2)。为mutate.m程序， 
+见附录。 
+
+4. 
+4. 
+44..试验与结果分析 
+
+     试验采用TSPLib标准库的eil51，eil76，eil101作为测试实例。每个实例分 
+别测试五次，求出平均值和最优值作为比较依据。 
+   问题        求解次数 最优理论  最优解                        最差解          平均值 
+                             解 
+  eil51          5          426       432.3981  445.9899  441.2703 
+  eil76          5          538       561.5283  576.1645  570.2783 
+  eil101         5          629       678.3685  699.7653  693.5415 
+     下面给出五次运行中的最优路径图： 
+
+      图1 eil51最优路径图                                图2 eil76最优路径图 
+
+----------------------- Page 6-----------------------
+
+                                图3 eil101最优路径图 
+
+     从图中可以看出，所获得路线都有交叉，明显不是最优路径。对于51个城市 
+和76个城市来说，都只有一个交叉，而对于101个城市来说，���叉比较多，求得 
+最优路径的结果也不是太理想。 
+     从参数设置来说，对于51个城市和76个城市，参数：群体总数s=400，交叉 
+概率Pc=0.9，变异概率Pm=0.2，最大迭代次数C=100。对于不断调整试验参数中 
+可以得出，最大迭代次数越多，结果比较接近理论最优解。 
+     对于101个城市，参数：群体总数s=500，交叉概率Pc=0.9，变异概率Pm=0.1， 
+最大迭代次数C=200。对于不断调整试验参数中可以得出，对于较大数目的城市 
+数，变异概率小一些，得到结果也会相应好一些。 
+
+5. 
+5. 
+55..结语 
+
+     本文运用Matlab软件，利用遗传算法解决了小规模的TSP问题。文章首先介 
+绍了TSP问题，并给出TSP问题的数学定义，然后介绍了遗传算法的原理以及算法 
+的基本过程，最后通过对标准TSPLib中的51、76和101个城市分别进行了测试， 
+根据试验对参数进行分析。本文程序解决小规模的TSP问题还可以，随着城市数 
+目的增大，计算精度有所下降，计算时间增长很快，效率较低较快，这也是下一 
+步需要改进的地方。 
+
+6. 
+6. 
+66..附录 
+
+%此为主程序代码，将下面各个程序分别保存在同一文件下运行，即可得出比较% 
+
+好的解，只是会有点交叉，但是最小路径值很接近最优理论值 
+function ga 
+
+----------------------- Page 7-----------------------
+
+s=500;%群体中个体数目 
+k=100;%选择优化个数 
+Pc=0.9;%交叉概率 
+Pm=0.1;%变异概率 
+C=200;%最大循环次数 
+[M,dista]=readfile('tsp76.txt');%读取城市坐标文件 
+[Ncities,b]=size(M); 
+t=Ncities+1; 
+farm=start(s,t);        %随机初始化种群 
+farm=qiujuli(farm,dista);%求出种群的适应度 
+counter=0; 
+while counter<=C 
+     counter=counter+1; 
+     farm=select(farm,k);%选择 
+     if(Pc>rand) 
+         farm=cross(farm,dista);%交叉 
+     end 
+     if(Pm>rand) 
+         farm=mutate(farm,dista);%变异 
+     end 
+end 
+[a,b]=size(farm); %求出总路径中的最小值 
+A=zeros(1,a); 
+for i=1:a 
+     A(1,i)=farm(i,b); 
+end 
+shortest_path=min(A)%最短路径值 
+[c,d]=find(A==shortest_path); 
+e=c(1);%画出最短路径图 
+for i=1:Ncities 
+     plot(M(i,1),M(i,2),'ro');%‘ro’可改 
+     hold on 
+end 
+
+for i=1:t-2 
+     plot_ga(farm(e,1),farm(e,t-1),M); 
+     plot_ga(farm(e,i),farm(e,i+1),M); 
+end 
+
+----------------------------------------------------- 
+---------------------------------------------------- 
+%读取城市坐标的txt文件，格式为n*2的坐标，n为城市数目，%filename为文件 
+
+名，返回n*2的坐标pop，以及城市距离矩阵%city_distance，为n*n 
+
+----------------------- Page 8-----------------------
+
+function [pop,city_distance]=readfile(filename) 
+fid=fopen(filename,'r'); 
+pop=fscanf(fid,'%d',[2 inf]); 
+pop=pop'; 
+[a,b]=size(pop); 
+city_distance=zeros(a,a); 
+for i=1:a 
+     for j=i:a 
+         city_distance(i,j)=sqrt((pop(i,1)-pop(j,1))^2+(pop(i,2)-pop 
+
+(j,2))^2); 
+         city_distance(j,i)=city_distance(i,j); 
+     end 
+end 
+fclose(fid) 
+
+--------------------------------------------------------------------- 
+-------------------------------------------------- 
+%生成s*t列的种群，其中每一行的前t-1个数为1到t-1的随机不重复排列，用于 
+%显示随机行走的路径，最后一列记录这样走的总距离，一般t等于城市数目+1， 
+%s为初始设置的群体中个体数目 
+function pop=start(s,t) 
+pop=zeros(s,t); 
+for i=1:s 
+pop(i,1:t-1)=randperm(t-1); 
+end 
+
+--------------------------------------------------------------------- 
+- 
+
+                                编码，生成 个小于 的整数数组 
+function [a]=bianma(k,u)  %              k       u 
+a=zeros(1,k); 
+aa=0:u-1; 
+for i=1:k 
+    point=round(rand*(u-i))+1; 
+    a(i)=aa(point); 
+    aa(point)=[]; 
+end 
+
+================================================================ 
+
+-------------------------------------------------- 
+
+----------------------- Page 9-----------------------
+
+%该算法主要实现pop矩阵中最后一列的值，即总距离 
+function [pop]=qiujuli(pop,D) %D为城市的距离矩阵，pop为种群 
+[s,t]=size(pop); 
+for i=1:s 
+     dd=0; 
+     for j=1:t-2 
+         dd=D(pop(i,j),pop(i,j+1))+dd; 
+     end 
+     dd=dd+D(pop(i,1),pop(i,t-1));                    %dd为每个访遍城市路径 
+的 
+
+适应度 
+     pop(i,t)=dd;                                    %存储适应度 
+end 
+
+--------------------------------------------------------------------- 
+- 
+
+-------------------------------------------------- 
+%该算法是将群体中适应度最大的k个个体直接替换适应度最小的k个%个��� 
+function [pop]=select(pop,k) 
+[s,t]=size(pop); 
+m11=(pop(:,t)); 
+m11=m11'; 
+mmax=zeros(1,k); 
+mmin=zeros(1,k); 
+num=1; 
+while num<k+1; 
+[a,mmax(num)]=max(m11); 
+m11(mmax(num))=0; 
+num=num+1; 
+end 
+num=1; 
+while num<k+1; 
+[b,mmin(num)]=min(m11); 
+m11(mmin(num))=a; 
+num=num+1; 
+end 
+for i=1:k 
+pop(mmax(i),:)=pop(mmin(i),:); 
+end 
+
+----------------------- Page 10-----------------------
+
+--------------------------------------------------------------------- 
+- 
+
+-------------------------------------------------- 
+%交叉算法，主要思想是：每两行进行交叉的操作，使每行的适应度%减少 
+function [pop]=cross(pop,D) 
+[s,t]=size(pop); 
+pop1=pop;%保存原群体矩阵 
+m=zeros(1,t);%初始化交叉后第一个个体 
+n=zeros(1,t);%初始化交叉后第二个个体 
+for i=1:2:s %每两行进行交叉操作 
+     x1=pop(i,:); 
+     y1=pop(i+1,:); 
+     x2=pop(i,:); 
+     y2=pop(i+1,:); 
+     c1=round(rand*(t-2))+1; %生成1到t-1之间的随机整数 
+     c2=c1; 
+     m(1)=c1; 
+     n(1)=c2; 
+     j=2; 
+     while size(x1,2)>2           %判断是否继续 
+         l=find(x1==c1); 
+         h=find(x2==c2); 
+         if l==t+1-j 
+              lr=1; 
+         else 
+              lr=l+1; 
+         end 
+         if h==1 
+              hle=t+1-j; 
+         else 
+              hle=h-1; 
+         end 
+         q=find(y1==c1); 
+         z=find(y2==c2); 
+         if q==t+1-j 
+              qr=1; 
+         else qr=q+1; 
+         end 
+         if z==1 
+              zle=t+1-j; 
+         else zle=z-1; 
+         end 
+         if D(c1,x1(lr))<D(c1,y1(qr)) %根据比较两点间距离进行交换 
+
+----------------------- Page 11-----------------------
+
+              m(j)=x1(lr); 
+              c1=x1(lr); 
+         else 
+              m(j)=y1(qr);c1=y1(qr); 
+         end 
+         x1(l)=[];                        %删除父节点 
+         y1(q)=[];                        %删除父节点 
+         if D(c2,x2(hle))<D(c2,y2(zle))%根据比较两点间距离进行交换 
+              n(j)=x2(hle); 
+              c2=x2(hle); 
+         else 
+              n(j)=y2(zle);c2=y2(zle); 
+         end 
+         x2(h)=[];%删除父节点 
+         y2(z)=[];%删除父节点 
+         j=j+1; 
+     end 
+     pop1(i,:)=m; 
+     pop1(i+1,:)=n; 
+end 
+pop1=qiujuli(pop1,D);%求出交叉后的路径矩阵 
+for i=1:s               %选择适应度变小的交叉，进行更新 
+     if pop1(i,t)<pop(i,t) 
+         pop(i,:)=pop1(i,:); 
+     end 
+end 
+
+--------------------------------------------------------------------- 
+- 
+
+--------------------------------------------------------------------- 
+- 
+%变异，D为城市的距离矩阵，进行变异 
+function [pop]=mutate(pop,D) 
+[s,t]=size(pop); 
+pop1=pop; 
+for i=1:s 
+mutatepoint=bianma(2); 
+b=round((mutatepoint(2)-mutatepoint(1))/2-0.5); 
+for j=1:b 
+zhong=pop1(i,mutatepoint(1)+j); 
+pop1(i,mutatepoint(1)+j)=pop1(i,mutatepoint(2)-j); 
+pop1(i,mutatepoint(2)-j)=zhong; 
+end 
+
+----------------------- Page 12-----------------------
+
+end 
+pop1=qiujuli(pop,D); 
+for i=1:s 
+if pop1(i,t)<pop(i,t) 
+pop(i,:)=pop1(i,:) 
+end 
+end 
+
+--------------------------------------------------------------------- 
+- 
+
+--------------------------------------------------------------------- 
+- 
+%画图 
+function plot_ga(a,b,V) 
+P=[V(a,1) V(a,2)]; 
+Q=[V(b,1) V(b,2)]; 
+c=P(1,2); 
+P(1,2)=Q(1,1); 
+Q(1,1)=c; 
+plot(P,Q,'g-')               %g表示颜色，-表示连线的形状，可改。 
+hold on