// All the values of the Target output
// are accessible through the array:
// aOutputTarget[0] = 0
// aOutputTarget[1] = 1
// aOutputTarget[2] = 0
// etc.

// All the values of the Model output
// are accessible through the array:
// aOutputModel[0] = 0
// aOutputModel[1] = 1
// aOutputModel[2] = 1
// etc.

// Essential and useful parameters you may use
// to design your fitness function:
// aParameters[0] = number of records
// aParameters[1] = averaged target output
// aParameters[2] = variance of the target output
// aParameters[3] = 0/1 rounding threshold
// aParameters[4] = number of samples in the predominant class
// aParameters[5] = minimum program size
// aParameters[6] = maximum program size

// Useful information about the evolving models
// you may use to design your fitness function:
// aModelInfo[0] = program size
// aModelInfo[1] = used variables
// aModelInfo[2] = number of literals

// Your custom fitness function must return a value, for example:
// return fitness;

// Below is an example of a correct fitness function, more specifically,
// the Hits with Punishment fitness function of GeneXproTools, for which 
// the maximum fitness is equal to 1000:

// HITS WITH PUNISHMENT/REWARD
var nRecords = aParameters[0];
var roundingThreshold = aParameters[3];
var fitness = 0.0;
var hits = 0; 
var fitnessHits = 0.0; 
var reward = 1.0; // must be greater than zero 
var punishment = -2.0; // is usually a negative value
var rewardPunishmentBallance = 0.0;
var truePositives = 0;
var trueNegatives = 0;
for (var nR=0; nR<nRecords; nR++)
{
    // Convert the value returned by the model into 1 or 0 
    if (aOutputModel[nR] >= roundingThreshold)
    {
        aOutputModel[nR] = 1;
    }
    else
    {
        aOutputModel[nR] = 0;
    }
    // Evaluation of the fitness components
    if (aOutputModel[nR] == aOutputTarget[nR])                   
    {
        hits++;
        rewardPunishmentBallance = rewardPunishmentBallance + reward;
        if(aOutputTarget[nR] == 1) 
        {
            truePositives++;
        }
        else
        {
            trueNegatives++;
        }
    }
    else
    {
        rewardPunishmentBallance = rewardPunishmentBallance + punishment;
    }
} //for nR
    
// Fitness evaluation & normalization
// Fitness from Punishments/Rewards
if (rewardPunishmentBallance < 0.0) rewardPunishmentBallance = 0.0;
rewardPunishmentBallance = rewardPunishmentBallance / (nRecords * reward);
    
// Fitness from Hits
fitnessHits = hits / nRecords;
    
// Final fitness
fitness = ((fitnessHits + rewardPunishmentBallance)/2.0) * 1000.0; 
    
// Death Penalty
if ((truePositives == 0) || (trueNegatives == 0))
{
    fitness = 0.0;
}
    
return fitness;