/// <summary>
/// Select two parents, based upon fitness, for a crossover operation.
/// Place the two children into the new Population.
/// </summary>
/// <param name="PopNew">Population to add the newly created program to</param>
private void Crossover(GPPopulation PopNew, GPFitnessObjectiveBase FitnessSelection)
{
GPProgram Child1 = (GPProgram)FitnessSelection.Programs(ExecProgramSelection()).Clone();
GPProgram Child2 = (GPProgram)FitnessSelection.Programs(ExecProgramSelection()).Clone();
//
// Convert to trees
Child1.ConvertToTree(m_ModelerConfig.FunctionSet, false);
Child2.ConvertToTree(m_ModelerConfig.FunctionSet, false);
//
// Perform the crossover
m_TreeFactory.Attach(Child1);
Child2 = m_TreeFactory.Crossover(Child2);
//
// Convert to arrays
Child1.ConvertToArray(m_ModelerConfig.FunctionSet);
Child2.ConvertToArray(m_ModelerConfig.FunctionSet);
//
// Add them to the new Population
PopNew.Programs.Add(Child1);
if (PopNew.Count < m_PopCurrent.Count)
{
PopNew.Programs.Add(Child2);
}
}
/// <summary>
/// Select a program based upon fitness to reproduce into the next generation.
/// </summary>
/// <param name="PopNew">Population to add the reproduced program into</param>
private void Reproduce(GPPopulation PopNew, GPFitnessObjectiveBase FitnessSelection)
{
GPProgram Copy = (GPProgram)FitnessSelection.Programs(ExecProgramSelection()).Clone();
//
// Add this copied child into the new Population
PopNew.Programs.Add(Copy);
}
/// <summary>
/// Works through the Population to compute the fitness for each program, keeping
/// track of a few statistics, such as best program, worst fitness and
/// average fitness. Makes a call to the abstract method "ComputeFitness" that
/// must be implemented by all GPFitness derived classes.
/// </summary>
/// <param name="Generation">Current modeling generation</param>
/// <param name="Population">Current generation Population of programs</param>
/// <returns></returns>
private int EvaluatePopulation(int Generation, GPPopulation Population)
{
//
// Check to see if the Population size changed, if so, reallocate the array
// that holds the fitness values
if (Population.Count != m_PrevPopulationSize)
{
if (m_Config.Profile.SPEA2MultiObjective)
{
m_FitnessSelection = new GPFitnessSPEA2(Population.Count);
}
else
{
m_FitnessSelection = new GPFitnessSingle(Population.Count);
}
}
FitnessMaximum = 0.0;
FitnessAverage = 0.0;
//
// Go through each program in the Population. First, compute the raw
// fitness result of the program. Once that is done, a call to the
// abstract ComputeFitness function is made where the User Defined Fitness
// function is called.
m_BestProgramGeneration = 0;
m_WorstProgramGeneration = 0;
m_CurrentProgram = 0;
m_CompletedPrograms = 0;
m_Population = Population;
//
// Create the event to be signed when the last program is done processing
wh_LastProgramDone = new EventWaitHandle(false, EventResetMode.ManualReset);
//
// Signal the suspend event
wh_Suspend.Set();
//
// Wait for the last program to be computed
wh_LastProgramDone.WaitOne();
//
// Finish up the average Population fitness
FitnessAverage /= Population.Count;
return(m_BestProgramGeneration);
}
/// <summary>
/// Select a program, based on fitness, for mutation. Place
// the mutated program into the new Population.
/// </summary>
/// <param name="PopNew">Population to add the newly created program to</param>
private void Mutate(GPPopulation PopNew, GPFitnessObjectiveBase FitnessSelection)
{
GPProgram Copy = (GPProgram)FitnessSelection.Programs(ExecProgramSelection()).Clone();
//
// Convert it to a tree
Copy.ConvertToTree(m_ModelerConfig.FunctionSet, false);
//
// Perform the mutation
m_TreeFactory.Attach(Copy);
m_TreeFactory.Mutate();
//
// Return it back to an array
Copy.ConvertToArray(m_ModelerConfig.FunctionSet);
//
// Add this mutated individual to the new Population
PopNew.Programs.Add(Copy);
}
