/// <summary>
/// Evaluates the program versus each set of training inputs and
/// computes the custom fitness of the program.
/// </summary>
/// <param name="ProgramID">Index of the program in the Population</param>
/// <param name="Predictions">Set of predicted values by the program</param>
/// <returns>Custom fitness of the program</returns>
private double ComputeProgramFitness(int ProgramID, double[] Predictions)
{
GPProgram Program = m_Population.Programs[ProgramID];
//
// Start by converting the program into a tree representation
Program.ConvertToTree(m_Config.FunctionSet, true);
//
// Set the memory size
Program.CountMemory = m_Config.Profile.CountMemory;
//
// Test each item in the training data set
m_FitnessHits[ProgramID] = 0;
for (int FitnessTest = 0; FitnessTest < m_TrainingData.Rows && !m_Abort; FitnessTest++)
{
//
// Assign the input values to the program
Program.UserTerminals = m_TrainingData.InputRow(FitnessTest);
//
// Assign a reference to the historical set of data for this fitness test
if (m_UseInputHistory)
{
Program.InputHistory = m_UserInputHistory[FitnessTest];
}
//
// This is the money shot, execute the genetic program!
double Result = Program.EvaluateAsDouble();
//
// Deal with problems that might have come up during the fitness computation
if (double.IsInfinity(Result) ||
double.IsNaN(Result) ||
double.IsNegativeInfinity(Result) ||
double.IsPositiveInfinity(Result))
{
Result = m_MaximumError;
}
//
// If the program size is above our threshold size, give it the
// worst possible result to prevent it from being used in any of the
// genetic operations. There is a problem if the number of nodes in
// the tree gets about the size of a 'short', we loose the ability to
// count and label them. Question...why not use an 'int'? Two reasons...
// 1. A program bigger than 16bits of nodes is ridiculous in the first place
// 2. Memory, each node would require 32bits for a lable, instead of 16bits,
// it doubles that bit of storage, which we don't need.
if (Program.CountNodes >= GPEnums.PROGRAMSIZE_THRESHOLD)
{
Result = m_MaximumError;
}
//
// Store the Program result
Predictions[FitnessTest] = Result;
//
// Determine if we have a "hit" against the input data
double Error = Math.Abs(Result - m_TrainingData.ObjectiveRow(FitnessTest)[0]);
if (Error <= m_Tolerance && Error >= -m_Tolerance)
{
m_FitnessHits[ProgramID]++;
}
}
//
// Restore it back to an array.
Program.ConvertToArray(m_Config.FunctionSet);
//
// Make a call into the custom fitness object to evaluate the
// fitness of the program.
double ProgramFitness = m_Config.Fitness.ComputeFitness(
m_UserInputHistoryCustomFitness,
Predictions,
m_TrainingData.ObjectiveColumn(0),
m_TrainingAverage,
m_Tolerance);
//
// Still have to check the result for problems
if (double.IsNaN(ProgramFitness) || double.IsInfinity(ProgramFitness) || double.IsPositiveInfinity(ProgramFitness) || double.IsNegativeInfinity(ProgramFitness))
{
ProgramFitness = m_FitnessMeasure[m_WorstProgramGeneration];
}
return(ProgramFitness);
}
public double[] InputRow(int Row)
{
return(m_Training.InputRow(Row));
}