Fitness for a genetic algorithm refuses to budge

Fitness for a genetic algorithm refuses to budge

By : juanpa
Date : November 17 2020, 01:00 AM
this will help Let's have a closer look at the snippet of your static getFitness(Chromosome chrom) function:
code :
for(int x = 0; x< solution.length;x++){
  return fitness += Math.abs((int)(chrom.getGene(x)) - (int)(solution[x]));
return fitness;
if(FitnessCalc.getFitness(getChromosome(x)) >= FitnessCalc.getFitness(fittest)){
  fittest =  getChromosome(x);

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Tracking fitness in a genetic algorithm

Tracking fitness in a genetic algorithm

By : Christopher Auer
Date : March 29 2020, 07:55 AM
it should still fix some issue
what I can't figure out is how to store the fitness so that I know when I've tried a combination before.
Linear fitness scaling in Genetic Algorithm produces negative fitness values

Linear fitness scaling in Genetic Algorithm produces negative fitness values

By : user3288470
Date : March 29 2020, 07:55 AM
seems to work fine Your smallest possible value for u = 5 - (2*5.12^2). Why not just add this to your u?
Fitness function using php in genetic algorithm

Fitness function using php in genetic algorithm

By : Pankaj Bhirud
Date : March 29 2020, 07:55 AM
hop of those help? Here's an example implementation of your formula. There are two glaring problems with your question however, so let's go through those first:
How on earth are your arrays actually structured? Your examples aren't very clear - arrays only have one key and one value. In my example I've interpreted your examples as literal values, and I've split them into the numbers in the function. Adjust to suit. How do you want this formula to scale up depending on how many employees are selected? My implementation has the first and second entries hard coded, then it would scale automatically after that by simply adding the "population" on for each value after the first two. A bonus question/problem: your formula has a bracket at the end, which from a glance, followed by a glance over the formula from left to right seems to imply that the second half of your formula is added and divided by N, which is incorrect because your expected output values occur when the entire result is divided by N.
code :
function doMyCalculation($selections) {
    // Get number of selected employees
    $num_selected = count($selections);

    // Break up the format of your array - what is it supposed to be?
    array_walk($selections, function(&$employee) {
        list($emp, $val) = explode('=>', $employee);
        $employee = (int) $val;

    // Initialize variable
    $return = 0;
    // Loop through all "employees"
    for($i = 0; $i < $num_selected; $i++) {
        // For the first two, we're going to use N as a multiplier
        if($i < 2) 
             // Use [N - current] as the multiplier (only twice)
            $return += ($num_selected - $i) * $selections[$i];
            // Otherwise, just add it normally
            $return += $selections[$i];

    // Divide the whole lot by N
    $return /= $num_selected;

    return $return;

echo doMyCalculation($arr1); // 373.25
echo doMyCalculation($arr2); // 327
echo doMyCalculation($arr3); // 319.25
genetic algorithm - fitness function

genetic algorithm - fitness function

By : user3625584
Date : March 29 2020, 07:55 AM
this will help Are you sure you are not mixing genetic algorithms with traditional pathfinding algorithms? Both have sort of a fitness function. In the case of pathfinding, "heuristic" is often what it's called. For those algorithms, the "fitness" has to be assumed to use while executing the algorithm (or more like to speed it up).
The fitness function in actual genetic algorithms is relatively exact but that's because it is usually more of an evaluation. For example you perform the simulation with the current values and compute how long it took for the mouse to find the cheese. That time or distance traveled is the inverse fitness (inverse because larger number means less fit). If the fitness was better than previous runs, you can branch off new simulations with slightly different settings (generated via mutation, randomness etc.) and evaluate each time what improves the fitness and what doesn't (-> Survival of the fittest). If the fitness was worse, give this branch up (or maybe keep it only every XX times you encounter a bad branch; "possibly it is on a good way").
designing fitness function in genetic algorithm

designing fitness function in genetic algorithm

By : Nipuna
Date : March 29 2020, 07:55 AM
will be helpful for those in need I guess you are referring to a system of 5 linear equations with 7 variables.
This paper seems to show what you're looking for. You basically need to define a cost function and use the GA to minimize it. Search the pdf for "fitness function" to see exactly how to do this. The idea is to find some measure of how well your set of variable approximates the solution (or a solution in your case) for the system.
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