GeneXproTools 4.0 gives you access to a wide set of 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[4] = number of samples in the predominant class (function output 0 or 1)
• aParameters[5] = minimum program size
• aParameters[6] = maximum program size

In addition, GeneXproTools also gives you access to useful information about the evolving models that are essential for designing custom fitness functions with parsimony pressure:

• aModelInfo[0] = program size
• aModelInfo[1] = used variables
• aModelInfo[2] = number of literals

The code for the custom fitness function must be in JavaScript and can be tested before evolving a model with it. Please remember that GeneXproTools 4.0 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 GeneXproTools 4.0.


     /////////////////////////////////////////////////////////
     // 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[4] = number of samples in the predominant class (function output 0 or 1)
     // 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 Number of Hits fitness function of GeneXproTools, for which 
     // the maximum fitness is equal to the number of sample cases:

     // NUMBER OF HITS
     var nSamples = aParameters[0];
     var fitness = 0.0;
     for (var i=0; i<nSamples; i++)
     {
          // Evaluate fitness 
          if (aOutputModel[i] == aOutputTarget[i])
          {
               fitness++;
          }
     }

     return fitness;

     /////////////////////////////////////////////////////////


Obviously, in this case, maximum fitness equals 1000 both for the training and testing sets, and you must also feed this information into GeneXproTools through the Custom Fitness Function window in the boxes Max. Training Fitness and Max. Testing Fitness.