Backpack (knapsack) by means of genetic algorithm. How to add penalty to the fitness func

Question

I use ga (matlab optimization tool) to solve the backpack problem. I wrote a simple fitness function with hardcoded weight-value array:

function fitness = bp_fitness(x)

% This function computes the fitness value for the 0-1 knapsack problem
% x: The current chromosome
% max_capacity: maximum capacity of the knapsack
% items: a two-dimensional array that holds the weight and value
% of each item respectively.

items = [8 15;2 16;5 20; 1 5;13 50;10 35;3 14;4 17;12 40;7 25;
     6 10;18 65;15 50;11 34;9 12;14 20;8 16;19 70;13 30;6 10;
     43 1;18 65;15 50;31 24;3 16;24 42;8 16;21 4;30 10;6 10;
     8 15;2 16];

max_capacity = 350;

overalweight = sum(x*items(:,1));

if overalweight > max_capacity
    fitness = 0;
else
    fitness = -1*sum(x*items(:,2));
end

and everything works ok. The question is, how to apply penalty for chromosomes, that doesn't fit the backpack? I've found this:

n the case of the backpack problem, the penalty can be calculated by multiplying the weight above the limit by the highest possible value to weight ratio.

K = max(vi / wi ) + c penalty = K * max[∑(w x )−W;0]

but I don't know how to implement this formula correctly. I've tried:

if overalweight > max_capacity
    k = max(items(:,2)./items(:,1))+1;
    penalty = k * (overalweight - max_capacity);
    fitness = -1*(sum(x*items(:,2)) - penalty);
    fprintf('fit = %i\n',-1*fitness);
else
    fitness = -1*sum(x*items(:,2));
end

This way there's no difference in the optimization results between two functions at all, which makes me think I did something wrong. Hope someone can help me here.