IS THIS A GOOD NSGA II CODE:
function [paretoFront, paretoSet] = NSGA2(NPop, T, lb, ub, dim)
    % Initialize population
    pop = initialization(NPop, dim, ub, lb);
    fitness = evaluate_population(pop, @zdt1);

    for gen = 1:T
        % Non-dominated sorting
        [fronts, ranks] = non_dominated_sort(fitness);
        
        % Create mating pool using tournament selection
        mating_pool = tournament_selection(pop, ranks, NPop);
        
        % Generate offspring using SBX crossover and polynomial mutation
        offspring = crossover(mating_pool, 0.9, dim);
        offspring = mutation(offspring, 1/dim, dim);
        
        % Evaluate offspring
        offspring_fitness = evaluate_population(offspring, @zdt1);
        
        % Combine population and offspring
        comb_pop = [pop; offspring];
        comb_fitness = [fitness; offspring_fitness];
        
        % Non-dominated sorting on combined population
        [fronts, ~] = non_dominated_sort(comb_fitness);
        
        % Create new population
        [pop, fitness] = select_new_population(comb_pop, comb_fitness, fronts, NPop);
        
        % Display generation progress
        if mod(gen, 10) == 0
            fprintf('Generation #%d/%d completed.\n', gen, T);
        end
    end

    % Return last population as Pareto front
    paretoFront = fitness;
    paretoSet = pop;
end

function pop = initialization(NPop, dim, ub, lb)
    pop = rand(NPop, dim) .* (ub - lb) + lb;
end

function fitness = evaluate_population(pop, FITNESSFCN)
    fitness = zeros(size(pop, 1), 2);  % Assuming 2 objectives
    for i = 1:size(pop, 1)
        fitness(i, :) = FITNESSFCN(pop(i, :));
    end
end

function [fronts, rank] = non_dominated_sort(fitness)
    num_individuals = size(fitness, 1);
    domination_count = zeros(num_individuals, 1);
    dominated_set = cell(num_individuals, 1);
    rank = zeros(num_individuals, 1);

    fronts = cell(1);
    for i = 1:num_individuals
        for j = 1:num_individuals
            if i ~= j
                if dominates(fitness(i, :), fitness(j, :))
                    dominated_set{i} = [dominated_set{i}, j];
                elseif dominates(fitness(j, :), fitness(i, :))
                    domination_count(i) = domination_count(i) + 1;
                end
            end
        end
        
        if domination_count(i) == 0
            rank(i) = 1;
            fronts{1} = [fronts{1}, i];
        end
    end
    
    current_front = 1;
    while ~isempty(fronts{current_front})
        next_front = [];
        for i = fronts{current_front}
            for j = dominated_set{i}
                domination_count(j) = domination_count(j) - 1;
                if domination_count(j) == 0
                    rank(j) = current_front + 1;
                    next_front = [next_front, j];
                end
            end
        end
        current_front = current_front + 1;
        fronts{current_front} = next_front;
    end
    fronts = fronts(1:current_front-1);
end

function is_dominated = dominates(x, y)
    is_dominated = all(x <= y) && any(x < y);
end

function selected_pop = tournament_selection(pop, ranks, num_selected)
    selected_pop = zeros(num_selected, size(pop, 2));
    for i = 1:num_selected
        candidates = randperm(size(pop, 1), 2);
        if ranks(candidates(1)) < ranks(candidates(2))
            selected_pop(i, :) = pop(candidates(1), :);
        else
            selected_pop(i, :) = pop(candidates(2), :);
        end
    end
end

function offspring = crossover(pop, crossover_prob, num_vars)
    % Implementation of Simulated Binary Crossover (SBX)
    eta_c = 20;  % Distribution index for crossover
    offspring = zeros(size(pop));
    
    for i = 1:2:size(pop, 1)
        if rand < crossover_prob
            parent1 = pop(i, :);
            parent2 = pop(i+1, :);
            
            % Initialize offspring
            child1 = zeros(1, num_vars);
            child2 = zeros(1, num_vars);
            
            % Perform SBX for each variable
            for j = 1:num_vars
                % Generate random number
                u = rand;
                
                % Calculate beta value
                if u <= 0.5
                    beta = (2*u)^(1/(eta_c+1));
                else
                    beta = (1/(2*(1-u)))^(1/(eta_c+1));
                end
                
                % Create offspring
                child1(j) = 0.5*((1+beta)*parent1(j) + (1-beta)*parent2(j));
                child2(j) = 0.5*((1-beta)*parent1(j) + (1+beta)*parent2(j));
                
                % Ensure bounds [0,1] are respected
                child1(j) = min(max(child1(j), 0), 1);
                child2(j) = min(max(child2(j), 0), 1);
            end
            
            offspring(i,:) = child1;
            offspring(i+1,:) = child2;
        else
            % If no crossover, copy parents
            offspring(i,:) = pop(i,:);
            offspring(i+1,:) = pop(i+1,:);
        end
    end
end


function mutated_pop = mutation(pop, mutation_prob, num_vars)
    eta_m = 20;  % Mutation distribution index
    mutated_pop = pop;
    for i = 1:size(pop, 1)
        for j = 1:num_vars
            if rand < mutation_prob
                r = rand;
                if r < 0.5
                    delta = (2 * r)^(1/(eta_m + 1)) - 1;
                else
                    delta = 1 - (2 * (1 - r))^(1/(eta_m + 1));
                end
                mutated_pop(i, j) = pop(i, j) + delta;
                mutated_pop(i, j) = min(max(mutated_pop(i, j), 0), 1);
            end
        end
    end
end

function [pop, fitness] = select_new_population(comb_pop, comb_fitness, fronts, NPop)
    pop = [];
    fitness = [];
    for i = 1:length(fronts)
        if size(pop, 1) + length(fronts{i}) > NPop
            break;
        end
        pop = [pop; comb_pop(fronts{i}, :)];
        fitness = [fitness; comb_fitness(fronts{i}, :)];
    end
    
    remaining = NPop - size(pop, 1);
    if remaining > 0
        selected_front = fronts{i};
        distances = crowding_distance(comb_fitness(selected_front, :));
        [~, sorted_indices] = sort(distances, 'descend');
        selected = selected_front(sorted_indices(1:remaining));
        pop = [pop; comb_pop(selected, :)];
        fitness = [fitness; comb_fitness(selected, :)];
    end
end

function distances = crowding_distance(fitness)
    num_individuals = size(fitness, 1);
    num_objectives = size(fitness, 2);
    distances = zeros(num_individuals, 1);
    
    for m = 1:num_objectives
        [sorted_values, sorted_idx] = sort(fitness(:, m));
        distances(sorted_idx(1)) = inf;
        distances(sorted_idx(end)) = inf;
        for i = 2:num_individuals-1
            if sorted_values(end) ~= sorted_values(1)
                distances(sorted_idx(i)) = distances(sorted_idx(i)) + ...
                    (sorted_values(i+1) - sorted_values(i-1)) / (sorted_values(end) - sorted_values(1));
            end
        end
    end
end