For all classification problems, in order to be able to apply a particular fitness function, the learning algorithms APS 3.0 must convert the value returned by the evolved model into “1” or “0” using the 0/1 Rounding Threshold. If the value returned by the evolved model is equal to or greater than the rounding threshold, then the record is classified as “1”, “0” otherwise.

Thus, the 0/1 Rounding Threshold is an integral part of all fitness functions used for classification and must be appropriately set in the Settings Panel -> Fitness Function Tab.

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 number of hits fitness function of APS 3.0.


     // 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 output of the Model
     // 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 samples
     // 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

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

     // Below is an example of a correct fitness function, more specifically,
     // the Number of Hits fitness function of APS 3.0, for which 
     // the maximum fitness is equal to the number of sample cases:

     // NUMBER OF HITS
     var nSamples = aParameters[0];
     var roundingThreshold = aParameters[3];
     var fitness = 0.0;
     for (var i=0; i<nSamples; i++)
     {
          // Convert the value returned by the model into 1 or 0 
          if (aOutputModel[i] >= roundingThreshold)
          {
               aOutputModel[i] = 1;
          }
          else
          {
               aOutputModel[i] = 0;
          }
          // Evaluate fitness 
          if (aOutputModel[i] == aOutputTarget[i]) 
          {
               fitness++;
          }
     }

     // Exclude uninteresting models
     var limitViability = aParameters[4];
     if (fitness <= limitViability) 
     {
          fitness = 0;
     }

     return fitness;


Obviously, in this case, maximum fitness equals the number of samples in your data.