algorithm - How to implement Roulette Wheel Selection and Rank Sleection on Matlab code for the Traveling Salesman Problom? -


i have assignment coding genetic algorithm traveling salesman problem. i've written code giving correct results using tournament selection. problem is, have wheel , rank , results incorrect.

here code using tournament selection:

clc; clear all; close all;  nofcities = 30; initialpopulationsize = nofcities*nofcities; generations = nofcities*ceil(nofcities/10);  cities = floor(rand([nofcities 2])*100+1);  figure; hold on; scatter(cities(:,1), cities(:,2), 5, 'b','fill'); line(cities(:,1), cities(:,2)); line(cities([1 end],1), cities([1 end],2)); axis([0 110 0 110]);  population = zeros(initialpopulationsize ,nofcities);  i=1:initialpopulationsize     population(i,:) = randperm(nofcities); end  distancematrix = zeros(nofcities); i=1:nofcities     j=1:nofcities         if (i==j)             distancematrix(i,j)=0;         else             distancematrix(i,j) = sqrt((cities(i,1)-cities(j,1))^2+(cities(i,2)-cities(j,2))^2);         end     end end  u=1:generations          tourdistance = zeros(initialpopulationsize ,1);     i=1:initialpopulationsize         j=1:length(cities)-1            tourdistance(i) = tourdistance(i) + distancematrix(population(i,j),population(i,j+1));        end     end     i=1:initialpopulationsize          tourdistance(i) = tourdistance(i) + distancematrix(population(i,end),population(i,1));     end      min(tourdistance)      newpopulation = zeros(initialpopulationsize,nofcities);      k=1:initialpopulationsize         child = zeros(1,nofcities);         %tournament start         i=1:5            tournamentparent1(i) = ceil(rand()*initialpopulationsize);         end         p1 = find(tourdistance == min(tourdistance([tournamentparent1])));         parent1 = population(p1(1), :);         i=1:5            tournamentparent2(i) = ceil(rand()*initialpopulationsize);         end         p2 = find(tourdistance == min(tourdistance([tournamentparent2])));         parent2 = population(p2(1), :);         %tournament end         %crossover         startpos = ceil(rand()*(nofcities/2));         endpos = ceil(rand()*(nofcities/2)+10);          i=1:nofcities            if (i>startpos && i<endpos)                 child(i) = parent1(i);            end         end          i=1:nofcities             if (isempty(find(child==parent2(i))))                 j=1:nofcities                     if (child(j) == 0)                         child(j) = parent2(i);                         break;                     end                 end             end         end          newpopulation(k,:) = child;     end      %mutation     mutationrate = 0.015;     i=1:initialpopulationsize        if (rand() < mutationrate)            pos1 = ceil(rand()*nofcities);            pos2 = ceil(rand()*nofcities);            mutation1 = newpopulation(i,pos1);            mutation2 = newpopulation(i,pos2);            newpopulation(i,pos1) = mutation2;            newpopulation(i,pos2) = mutation1;                   end     end      population = newpopulation;     u end  figure; hold on; scatter(cities(:,1), cities(:,2), 5, 'b','fill'); line(cities(population(i,:),1), cities(population(i,:),2)); line(cities([population(i,1) population(i,end)],1), cities([population(i,1) population(i,end)],2)); axis([0 110 0 110]);  %close all;

what want replace tournament code wheel , rank code.

here wrote wheel selection:

 fitness = tourdistance./sum(tourdistance);         wheel = cumsum(fitness);         parent1 = population(find(wheel >= rand(),1),:);         parent2 = population(find(wheel >= rand(),1),:);

for wheel selection work, should start designing fitness measure fitter individuals having bigger value. in contrast distance better individuals having smaller value. approach cumsum should work.

where issue ranking selection?


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