Matlab 水下无线光通信

len=640 % The length of the genomes popSize=500; % The size of the population (must be an even number) maxGens=100; % 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 => % % 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 �repositories� 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