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[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 MSE fitness function of GeneXproTools 4.0.


     // 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
     // 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 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 = 0.0;

     fitness = (1/(1+MSE))*1000;

     return fitness;


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