Error programs，download from mathworks （matlab 2015a）

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jixie zhuangbei 2015-8-4

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https://ww2.mathworks.cn/matlabcentral/answers/232223-error-programs-download-from-mathworks-matlab-2015a

评论： jixie zhuangbei 2015-8-30

% SpeedyGA is a vectorized implementation of a Simple Genetic Algorithm in Matlab
% Version 1.3
% Copyright (C) 2007, 2008, 2009  Keki Burjorjee
% Created and tested under Matlab 7 (R14). 
%  Licensed under the Apache License, Version 2.0 (the "License"); you may
%  not use this file except in compliance with the License. You may obtain 
%  a copy of the License at  
%
%   
%
%  Unless required by applicable law or agreed to in writing, software 
%  distributed under the License is distributed on an "AS IS" BASIS, 
%  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
%  See the License for the specific language governing permissions and 
%  limitations under the License. 
%  Acknowledgement of the author (Keki Burjorjee) is requested, but not required, 
%  in any publication that presents results obtained by using this script 
%  Without Sigma Scaling, Stochastic Universal Sampling, and the generation of mask 
%  repositories, SpeedyGA faithfully implements the specification of a simple genetic 
%  algorithm given on pages 10,11 of M. Mitchell's book An Introduction to
%  Genetic Algorithms, MIT Press, 1996). Selection is fitness
%  proportionate.
len=640                    % The length of the genomes  
popSize=500;               % The size of the population (must be an even number)
maxGens=1000;              % The maximum number of generations allowed in a run
probCrossover=1;           % The probability of crossing over. 
probMutation=0.003;        % The mutation probability (per bit)
sigmaScalingFlag=1;        % Sigma Scaling is described on pg 168 of M. Mitchell's
                           % GA book. It often improves GA performance.
sigmaScalingCoeff=1;       % Higher values => less fitness pressure 
SUSFlag=1;                 % 1 => Use Stochastic Universal Sampling (pg 168 of 
                           %      M. Mitchell's GA book)
                           % 0 => Do not use Stochastic Universal Sampling
                           %      Stochastic Universal Sampling almost always
                           %      improves performance
crossoverType=2;           % 0 => no crossover
                           % 1 => 1pt crossover
                           % 2 => uniform crossover
visualizationFlag=1;       % 0 => don't visualize bit frequencies
                           % 1 => visualize bit frequencies
verboseFlag=1;             % 1 => display details of each generation
                           % 0 => run quietly
useMaskRepositoriesFlag=1; % 1 => draw uniform crossover and mutation masks from 
                           %      a pregenerated repository of randomly generated bits. 
                           %      Significantly improves the speed of the code with
                           %      no apparent changes in the behavior of
                           %      the SGA
                           % 0 => generate uniform crossover and mutation
                           %      masks on the fly. Slower. 
% crossover masks to use if crossoverType==0.
mutationOnlycrossmasks=false(popSize,len);
% pre-generate two 搑epositories?of random binary digits from which the  
% the masks used in mutation and uniform crossover will be picked. 
% maskReposFactor determines the size of these repositories.
maskReposFactor=5;
uniformCrossmaskRepos=rand(popSize/2,(len+1)*maskReposFactor)<0.5;
mutmaskRepos=rand(popSize,(len+1)*maskReposFactor)<probMutation;
% preallocate vectors for recording the average and maximum fitness in each
% generation
avgFitnessHist=zeros(1,maxGens+1);
maxFitnessHist=zeros(1,maxGens+1);
eliteIndiv=[];
eliteFitness=-realmax;
% the population is a popSize by len matrix of randomly generated boolean
% values
pop=rand(popSize,len)<.5;
for gen=0:maxGens
      % evaluate the fitness of the population. The vector of fitness values 
      % returned  must be of dimensions 1 x popSize.
      fitnessVals=oneMax(pop);
      [maxFitnessHist(1,gen+1),maxIndex]=max(fitnessVals);    
      avgFitnessHist(1,gen+1)=mean(fitnessVals);
      if eliteFitness<maxFitnessHist(gen+1)
          eliteFitness=maxFitnessHist(gen+1);
          eliteIndiv=pop(maxIndex,:);
      end    
      % display the generation number, the average Fitness of the population,
      % and the maximum fitness of any individual in the population
      if verboseFlag
          display(['gen=' num2str(gen,'%.3d') '   avgFitness=' ...
              num2str(avgFitnessHist(1,gen+1),'%3.3f') '   maxFitness=' ...
              num2str(maxFitnessHist(1,gen+1),'%3.3f')]);
      end
      % Conditionally perform bit-frequency visualization
      if visualizationFlag
          figure(1)
          set (gcf, 'color', 'w');
          hold off
          bitFreqs=sum(pop)/popSize;
          plot(1:len,bitFreqs, '.');
          axis([0 len 0 1]);
          title(['Generation = ' num2str(gen) ', Average Fitness = ' sprintf('%0.3f', avgFitnessHist(1,gen+1))]);
          ylabel('Frequency of the Bit 1');
          xlabel('Locus');
          drawnow;
      end    
      % Conditionally perform sigma scaling 
      if sigmaScalingFlag
          sigma=std(fitnessVals);
          if sigma~=0;
              fitnessVals=1+(fitnessVals-mean(fitnessVals))/...
              (sigmaScalingCoeff*sigma);
              fitnessVals(fitnessVals<=0)=0;
          else
              fitnessVals=ones(popSize,1);
          end
      end        
      % Normalize the fitness values and then create an array with the 
      % cumulative normalized fitness values (the last value in this array
      % will be 1)
      cumNormFitnessVals=cumsum(fitnessVals/sum(fitnessVals));
      % Use fitness proportional selection with Stochastic Universal or Roulette
      % Wheel Sampling to determine the indices of the parents 
      % of all crossover operations
      if SUSFlag
          markers=rand(1,1)+[1:popSize]/popSize;
          markers(markers>1)=markers(markers>1)-1;
      else
          markers=rand(1,popSize);
      end
      [temp parentIndices]=histc(markers,[0 cumNormFitnessVals]);
      parentIndices=parentIndices(randperm(popSize));    
      % deterimine the first parents of each mating pair
      firstParents=pop(parentIndices(1:popSize/2),:);
      % determine the second parents of each mating pair
      secondParents=pop(parentIndices(popSize/2+1:end),:);
      % create crossover masks
      if crossoverType==0
          masks=mutationOnlycrossmasks;
      elseif crossoverType==1
          masks=false(popSize/2, len);
          temp=ceil(rand(popSize/2,1)*(len-1));
          for i=1:popSize/2
              masks(i,1:temp(i))=true;
          end
      else
          if useMaskRepositoriesFlag
              temp=floor(rand*len*(maskReposFactor-1));
              masks=uniformCrossmaskRepos(:,temp+1:temp+len);
          else
              masks=rand(popSize/2, len)<.5;
          end
      end
      % determine which parent pairs to leave uncrossed
      reprodIndices=rand(popSize/2,1)<1-probCrossover;
      masks(reprodIndices,:)=false;
      % implement crossover
      firstKids=firstParents;
      firstKids(masks)=secondParents(masks);
      secondKids=secondParents;
      secondKids(masks)=firstParents(masks);
      pop=[firstKids; secondKids];
      % implement mutation
      if useMaskRepositoriesFlag
          temp=floor(rand*len*(maskReposFactor-1));
          masks=mutmaskRepos(:,temp+1:temp+len);
      else
          masks=rand(popSize, len)<probMutation;
      end
      pop=xor(pop,masks);    
  end
  if verboseFlag
      figure(2)
      %set(gcf,'Color','w');
      hold off
      plot([0:maxGens],avgFitnessHist,'k-');
      hold on
      plot([0:maxGens],maxFitnessHist,'c-');
      title('Maximum and Average Fitness')
      xlabel('Generation')
      ylabel('Fitness')
  end
【The second：】
function [p_min, iter, f]=genetic_algorithm (func, numMaxInd, numF, numMaxGen, pRepr, pRecom, pMut, pImm, numVariabili, limLow, limUp, tolerance)
% A genetic algorithm is a search heuristic that mimics the process of 
% natural evolution. It is used to generate useful solutions to 
% optimization and search problems. Genetic algorithms belong to the larger
% class of evolutionary algorithms, which generate solutions to 
% optimization problems using techniques inspired by natural evolution, 
% such as inheritance, mutation, selection, and crossover.
%
% Output variables:
% - p_min: it's the minimum point of the objective function;
% - iter: it's the final iteration number;
% - f: it's the objective function value in the minimum point.
%
% Input variables:
% - func: it's the handle of the objective function to minimize (example: f_obj=@(x) function(x) where x is the variables vector); 
% - numMaxInd: Number of individuals (number of initial points);
% - numF: Number of sons for each generation;
% - numMaxGen: Max number of generations (number of max iterations);
% - pRepr: Reproduction probability;
% - pRecom: Ricombination probability;
% - pMut: Mutation probability;
% - pImm: Immigration probability;
% - numVariabili: it's the number of the function variables.
% - limLow: it's the low bound for the initial points;
% - limUp: it's the high bound for the initial points;
% - tolerance: it's the tolerance error for the stop criterion.
fOb=func;   % Objective Function (Function to minimize)
numInd=numMaxInd;   % Number of individuals (number of initial points)
numIter=numMaxGen;  % Number of generations (number of max iterations)
numFigliTot=numF;   % Number of sons for each generation.
% The sum of the probabilities of the mutation operations must be 1.
pRep=pRepr;     %Reproduction probability
pRec=pRecom;    %Ricombination probability
pM=pMut;    %Mutation probability
pI=pImm;    %Immigration probability
numVar=numVariabili;    %Number of function variables
low=limLow;
up=limUp;
% Initializes the initial population
ind=low+(up-low).*rand(numInd, numVar);
iter=1;
tolStop=tolerance;
stop=1;
while(iter<=numIter && stop>tolStop)
      %Calculates the population ranking
  sumValF=0;
  for l=1:numInd
      valF(l,1)=fOb(ind(l,:));
      sumValF=sumValF+(valF(l,1))^-1;
  end
  [valFord,Ind]=sort(valF,1,'ascend');
  indOrd=ind(Ind,:);
for l=1:numInd
    rank(l,1)=(valFord(l,1))^-1/sumValF;
end
      numFigli=1;
      numFigli=numFigli+1;
      while (numFigli<=numFigliTot)
          p=rand();   % Probability to choose one genetic operation
              if p>=0 && p<pRep
                 % disp('riprod')
                  % Reproduction
                  % Choose the parents with the wheel of fortune
                  gen=rand();
                  k=1;
                  r=0;
                  while(1)
                      r=r+rank(k,1);
                      if gen<=r;
                          break
                      else
                          k=k+1;
                      end
                  end
              newGen(numFigli,:)=indOrd(k,:);
              numFigli=numFigli+1;
              else if p>=pRep && p<(pRec+pRep)
                  % Ricombination  
                 % disp('ricomb')
                  minimo=low-1;
                  mass=up+1;
                  while(minimo<low || mass>up)
                      for h=1:2
                          gen=rand();
                          k=1;
                          r=0;
                          while(1)
                              r=r+rank(k,1);
                              if gen<=r;
                                  break
                              else
                                  k=k+1;
                              end
                          end
                      recomb(h,1)=k;
                      end
                  alpha=normrnd(0.8,0.5);     % Calculates the alpha coefficient with a gaussian distribution with average = 0.8 and sigma = 0.5
                  newI(1,:)=alpha.*indOrd(recomb(1,1),:)+(1-alpha).*indOrd(recomb(2,1),:);
                  newI(2,:)=alpha.*indOrd(recomb(2,1),:)+(1-alpha).*indOrd(recomb(1,1),:);
                  minimo=min(min(newI(1,:)),min(newI(2,:)));
                  mass=max(max(newI(1,:)),max(newI(2,:)));
                  end                
                  newGen(numFigli,:)=newI(1,:);
                  newGen(numFigli+1,:)=newI(2,:);               
                  numFigli=numFigli+2;
                  else if p>=(pRec+pRep) && p<(pRec+pRep+pM)
                  % Mutation
                 % disp('mutaz')
                  minimo=low-1;
                  mass=up+1;
                  while(minimo<low || mass>up)
                      gen=rand();
                      k=1;
                      r=0;
                  while(1)
                      r=r+rank(k,1);
                      if gen<=r;
                          break
                      else
                          k=k+1;
                      end
                  end                
                      newI=indOrd(k,:)+normrnd(0,0.8,1,numVar);    %Add to genotype random values ??created by considering a Gaussian distribution with zero mean and variance 0.8 
                      minimo=min(newI);
                      mass=max(newI);
                  end
                  newGen(numFigli,:)=newI(1,:);
                  numFigli=numFigli+1;
                      else if p>=(pRec+pRep+pM) && p<(pRec+pRep+pM+pI)
                  % Immigration
                 % disp('imm')
                 for l=1:50
                  newGen(numFigli,:)=low+(up-low).*rand(1, numVar);
                  numFigli=numFigli+1;
                 end
                          end
                      end
                  end
              end
      end
      %Calculation of the best individuals that will be part of the new population
      indTot=[ind;newGen];
      for l=1:size(indTot,1)
          valFtot(l,1)=fOb(indTot(l,:));
      end
      [valFnewOrd,IndNew]=sort(valFtot,1,'ascend');
      indOrdNew=indTot(IndNew,:);
     % disp('new gen')
      ind=indOrdNew(1:numInd,:);
      iter=iter+1;
      %stop=abs(f(1)-f(numInd));
      stop=valFnewOrd(1);
  end
f=valFnewOrd(1:numInd,:);
p_min=ind;