The code for the custom fitness function must be in JavaScript and can be tested before evolving a model with it. Please remember that APS 3.0 uses fitness proportionate selection and, therefore, fitness must increase with performance and only non-negative values are acceptable. Below is the sample code of a correct fitness function, which is identical to the MSE based fitness function of APS 3.0.


     // 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 output of the Model
     // 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 samples
     // aParameters[1] = averaged target output
     // aParameters[2] = variance of the target output

     // 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) based fitness function of APS,
     // for which the maximum fitness is equal to 1000:

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

     MSE = modelMinusTargetSquared / nSamples;

     if (MSE <= 0.000000001)
         MSE = 0;

     fitness = (1/(1+MSE))*1000;

     return fitness;


Obviously, in this case, maximum fitness equals 1000.