Genetic-Algorithm-Path-master.zip_任务分配 GA_任务分配算法_多旅行商问题_多目标分配_目标

function geneticAlgorithm(xMax, yMax, numOfChrom, generations, popSize, mutProb) %Setup the plots [pathHandle, geneHandle, distHandle] = setupPlots(xMax, yMax, numOfChrom, popSize, generations); %Generate the locations [start, stop, locations] = generateLocations(numOfChrom, xMax, yMax); %Generate initial population population = generatePopulation(numOfChrom, popSize); distHistory = zeros(generations, 1); %Starting a loop for i = 1:generations %Inside this loop, the first step is fitness evaluation %This will be the shortest distance fitValues = calculateFitness(start, stop, locations, population, popSize, numOfChrom); fitSave = population(:, find(fitValues == min(fitValues))); % min(fitValues) distHistory(i) = min(fitValues); %Convert the minimum fitValue to the selected path path = convertPathToCart(start, stop, locations, population, fitValues, numOfChrom); %Display the output before updating the population for the next %generation updatePlots(pathHandle, geneHandle, distHandle, start, stop, locations, path, population, generations); %Selection %Normalise the fitness so that they are a percentage of the overall %population normFitVals = normaliseFitness(fitValues, popSize); %Create the intermediate population interPop = generateIntermediatePopulation(population, normFitVals, numOfChrom, popSize); %Stochastic Sampling %Recombination % disp('Generate Child Population'); interPop = generateChildPopulation(interPop, numOfChrom, popSize); %Mutation % disp('Generate Mutation'); interPop = generateMutations(interPop, numOfChrom, popSize, mutProb); population = interPop; % set(distHandle, 'xdata', 1:generations, 'ydata', distHistory); % figure(3); clf;hold on; % plot(1:generations, distHistory, 'g'); % axis([0 generations 0 max(distHistory)]); % hold off; drawnow; % pause() population(:,end) = fitSave(:,1); % fitSave; end figure(3); clf;hold on; plot(1:generations, distHistory, 'g'); axis([0 generations 0 max(distHistory)]); hold off; end