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script.m
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script.m
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individuals = 50;
generations = 200;
pc = 0.8;
alfa = 0.5;
pm = 0.2;
sigma = 0.07;
tournamentParticipants = 3;
global data, global m, global n;
global maxValues;
data = load("data.txt");
[m,n] = size(data);
maxValues = findMaxGeneValues();
pop = genPop(individuals);
[bestFitIni, ~] = findBestCandidate(pop, "initial population");
vectorBestFit = zeros(1, generations);
for t = 1:generations
selPop = selectTournament(pop,tournamentParticipants);
O = crossoverPop(selPop, pc, alfa);
MO = mutatePop(O, pm, sigma);
MO = fitnessPop(MO);
pop = selectElitist(pop, MO);
[bestFitness, ~] = findBestCandidate(pop);
vectorBestFit(t) = bestFitness;
end
figure();
hist(pop(:,n));
title('Distribution of Final Population');
xlabel('Fitness (Profit in Lei)');
ylabel('Individuals');
figure();
plot(0:generations, [bestFitIni vectorBestFit]);
title('Population history');
xlabel('t (Generations)');
ylabel('Fitness (Profit in Lei)');
findBestCandidate(pop, "final population");