how to fix this code ??

MaxIt=100;  % Maximum Number of Iterations

nPop=25;  % Population Size

pc=0.8;                 % Crossover Percentage
nc=2*round(pc*nPop/2);  % Number of Offsprings (also Parnets)

pm=0.3;                 % Mutation Percentage
nm=round(pm*nPop);      % Number of Mutants
mu=0.02;                % Mutation Rate

CostFunction=@(x) Fitness_function(x);     % Cost Function
nVar=50;            % Number of Decision Variables
VarSize=[1 nVar];
empty_individual.Position=[];
empty_individual.Cost=[];

pop=repmat(empty_individual,nPop,1);

   for i=1:nPop

    % Initialize Position
    pop(i).Position=randi([0 1],m);

    % Evaluation
    pop(i).Cost=CostFunction(pop(i).Position);

end

% Sort Population
Costs=[pop.Cost];
[Costs, SortOrder]=sort(Costs);
pop=pop(SortOrder);

% Store Best Solution
BestSol=pop(1);

% Array to Hold Best Cost Values
BestCost=zeros(MaxIt,1);

% Store Cost
WorstCost=pop(end).Cost; 

    for it=1:MaxIt

    % Calculate Selection Probabilities
    if UseRouletteWheelSelection
        P=exp(-beta*Costs/WorstCost);
        P=P/sum(P);
    end

    % Crossover
    popc=repmat(empty_individual,nc/2,2);
    for k=1:nc/2

        % Select Parents Indices
        if UseRouletteWheelSelection
            i1=RouletteWheelSelection(P);
            i2=RouletteWheelSelection(P);
        end
        if UseTournamentSelection
            i1=TournamentSelection(pop,TournamentSize);
            i2=TournamentSelection(pop,TournamentSize);
        end
        if UseRandomSelection
            i1=randi([1 nPop]);
            i2=randi([1 nPop]);
        end

        % Select Parents
        p1=pop(i1);
        p2=pop(i2);

        % Perform Crossover
        [popc(k,1).Position, popc(k,2).Position]=Crossover(p1.Position,p2.Position);

        % Evaluate Offsprings
        popc(k,1).Cost=CostFunction(popc(k,1).Position);
        popc(k,2).Cost=CostFunction(popc(k,2).Position);

    end
    popc=popc(:);

    % Mutation
    popm=repmat(empty_individual,nm,1);
    for k=1:nm

        % Select Parent
        i=randi([1 nPop]);
        p=pop(i);

        % Perform Mutation
        popm(k).Position=Mutate(p.Position,mu);

        % Evaluate Mutant
        popm(k).Cost=CostFunction(popm(k).Position);

    end

    % Create Merged Population
    pop=[pop
         popc
         popm]; %#ok

    % Sort Population
    Costs=[pop.Cost];
    [Costs, SortOrder]=sort(Costs);
    pop=pop(SortOrder);

    % Update Worst Cost
    WorstCost=max(WorstCost,pop(end).Cost);

    % Truncation
    pop=pop(1:nPop);
    Costs=Costs(1:nPop);

    % Store Best Solution Ever Found
    BestSol=pop(1);

    % Store Best Cost Ever Found
    BestCost(it)=BestSol.Cost;

    % Show Iteration Information
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);

end

where the fitness_function is

function z = Fitness_function (x)
A = [ 0 1 0 1 0 0 1 0 0 1 ; 0 0 0 0 0 0 1 1 1 1 ; 1 1 1 0 1 1 1 1 1 1 ; 1 0 0 1 0 0 1 0 0 1 ; 1 1 0 1 0 1 0 0 1 1 ];

z = (sum(sum(A ~= x)));

end