// All the values of the Target output
// are accessible through the array:
// aOutputTarget[0] = 2.549
// aOutputTarget[1] = 5.215
// etc.

// All the values of the Model output
// are accessible through the array:
// aOutputModel[0] = 78.6945
// aOutputModel[1] = 12.6421
// 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[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: 
// the Mean Squared Error (MSE) fitness function of GeneXproTools,
// for which the maximum fitness is equal to 1000:

// MEAN SQUARED ERROR
var nRecords = aParameters[0];
var fitness = 0.0;
var modelMinusTargetSquared = 0.0;
var MSE = 0.0;
for (var nR=0; nR<nRecords; nR++)
{
    var temp1 = 0.0;
    temp1 = aOutputModel[nR] - aOutputTarget[nR]; 
    temp1 *= temp1;
    modelMinusTargetSquared += temp1;
} 

MSE = modelMinusTargetSquared / nRecords;

if (MSE <= 0.000000001)
    MSE = 0.0;

fitness = (1/(1+MSE))*1000;

return fitness;