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--- ai:lab2 [2024/10/08 14:02] – created tami
+++ ai:lab2 [2024/10/08 19:10] (current) – tami
@@ Line 1: / Line 1: @@
 ====== AI Lab 2 ======
+<WRAP column 30%>
+The following script produces the plot:
+{{ :ai:plot.jpg|}}
+X axis: Generations, Y Axis: Fitness
+</WRAP>
+<file matlab main.m>
+population_size = 100;
+chromosome_length = 500;
+generations = 500;
+mutation_rate = 0.001;
+best_fitness_hist = [];
+%create inital population
+population = initialization(population_size, chromosome_length);
+for g = 1:generations
+    %eval fitness
+    fitness = sum(population, 2);
+    %select individuals for tournament selection
+    selected = tournament_selection(population,fitness,population_size);
+    %crossover the individuals
+    offspring = crossover(selected);
+    %mutation
+    mutated_offspring = mutate(offspring, mutation_rate);
+    %create new population
+    population = mutated_offspring;
+    %display best fitness of this generation
+    best_fitness = max(fitness);
+    if best_fitness == chromosome_length
+        break
+    end
+    best_fitness_hist = [best_fitness_hist;best_fitness];
+    fprintf('Generation %d: Bestfitness = %d\n', g, best_fitness);
+end
+plot(best_fitness_hist)
+</file>
+</WRAP>
+<file matlab initialization.m>
+function population = initialization(size,string_length)
+    population = randi([0,1], size, string_length);
+end
+</file>
+<file matlab tournament_selection.m>
+function selected_parents = tournament_selection(population, fitness, n)
+    selected_parents = zeros(n, size(population, 2));
+    for i = 1:n
+        %randomly select 2 indiviuals
+        idx = randi([1, size(population, 1)], 2, 1);
+        if fitness(idx(1)) > fitness(idx(2))
+            selected_parents(i, :) = population(idx(1), :);
+        else
+            selected_parents(i, :) = population(idx(2), :);
+        end
+    end
+end
+</file>
+<file matlab crossover.m>
+function offspring = crossover(parents)
+    [num_parents, string_length] = size(parents);
+    offspring = zeros(num_parents, string_length);
+    %pick every 2, use -1 so you can select the next one i.e. i+1 without
+    %running out of hte index bounds
+    for i = 1:2:num_parents-1
+        crossover_point = randi([1, string_length-1]);
+        offspring(i, :) = [
+            parents(i, 1:crossover_point) ...
+            parents(i+1, crossover_point+1:end)
+        ];
+        offspring(i+1, :) = [
+            parents(i+1, 1:crossover_point) ...
+            parents(i, crossover_point+1:end)
+        ];
+    end
+    if mod(num_parents, 2) == 1
+        %Carry over last parent if odd
+        offspring(end, :) = parents(end, :);
+    end
+end
+</file>
+<file matlab mutate.m>
+function mutated_offspring = mutate(offspring, mutation_rate)
+    mutated_offspring = offspring;
+    for i = 1:size(offspring, 1)
+        for j = 1:size(offspring, 2)
+            if rand < mutation_rate
+                % flip the bits
+                mutated_offspring(i, j) = ~mutated_offspring(i,j);
+            end
+        end
+    end
+end
+</file>