protected internal virtual GAIndividual ReproduceBySimplification(int inIndex)
{
PushGPIndividual i = (PushGPIndividual)ReproduceByClone(inIndex);
i = Autosimplify(i, _reproductionSimplifications);
return(i);
}
protected internal override GAIndividual ReproduceByMutation(int inIndex)
{
PushGPIndividual i = (PushGPIndividual)ReproduceByClone(inIndex);
int totalsize = i._program.ProgramSize();
int which = ReproductionNodeSelection(i);
int oldsize = i._program.SubtreeSize(which);
int newsize = 0;
if (_useFairMutation)
{
int range = (int)Math.Max(1, _fairMutationRange * oldsize);
newsize = Math.Max(1, oldsize + Rng.Next(2 * range) - range);
}
else
{
newsize = Rng.Next(_maxRandomCodeSize);
}
object newtree;
if (newsize == 1)
{
newtree = _interpreter.randProgram.RandomAtom();
}
else
{
newtree = _interpreter.randProgram.RandomCode(newsize);
}
if (newsize + totalsize - oldsize <= _maxPointsInProgram)
{
i._program.ReplaceSubtree(which, newtree);
}
return(i);
}
protected internal override void InitIndividual(GAIndividual inIndividual)
{
PushGPIndividual i = (PushGPIndividual)inIndividual;
int randomCodeSize = Rng.Next(_maxRandomCodeSize) + 2;
Program p = _interpreter.randProgram.RandomCode(randomCodeSize);
i.SetProgram(p);
}
public virtual void RunTestProgram(Program p, int inTestCaseIndex)
{
PushGPIndividual i = new PushGPIndividual(p);
GATestCase test = _testCases[inTestCaseIndex];
Console.Out.WriteLine("Executing program: " + p);
EvaluateTestCase(i, test._input, test._output);
Console.Out.WriteLine(_interpreter);
}
protected internal virtual PushGPIndividual Autosimplify(PushGPIndividual inIndividual, int steps)
{
PushGPIndividual simplest = (PushGPIndividual)inIndividual.Clone();
PushGPIndividual trial = (PushGPIndividual)inIndividual.Clone();
EvaluateIndividual(simplest, true);
float bestError = simplest.GetFitness();
bool madeSimpler = false;
for (int i = 0; i < steps; i++)
{
madeSimpler = false;
float method = Rng.Next(100);
if (trial._program.ProgramSize() <= 0)
{
break;
}
if (method < _simplifyFlattenPercent)
{
// Flatten random thing
int pointIndex = Rng.Next(trial._program.ProgramSize());
object point = trial._program.Subtree(pointIndex);
if (point is Program)
{
trial._program.Flatten(pointIndex);
madeSimpler = true;
}
}
else
{
// Remove small number of random things
int numberToRemove = Rng.Next(3) + 1;
for (int j = 0; j < numberToRemove; j++)
{
int trialSize = trial._program.ProgramSize();
if (trialSize > 0)
{
int pointIndex = Rng.Next(trialSize);
trial._program.ReplaceSubtree(pointIndex, new Program());
trial._program.Flatten(pointIndex);
madeSimpler = true;
}
}
}
if (madeSimpler)
{
EvaluateIndividual(trial, true);
if (trial.GetFitness() <= bestError)
{
simplest = (PushGPIndividual)trial.Clone();
bestError = trial.GetFitness();
}
}
trial = (PushGPIndividual)simplest.Clone();
}
return(simplest);
}
/// <summary>Selects a node to use during crossover or mutation.</summary>
/// <remarks>
/// Selects a node to use during crossover or mutation. The selection
/// mechanism depends on the global parameter _nodeSelectionMode.
/// </remarks>
/// <param name="inInd">= Individual to select node from.</param>
/// <returns>Index of the node to use for reproduction.</returns>
protected internal virtual int ReproductionNodeSelection(PushGPIndividual inInd)
{
int totalSize = inInd._program.ProgramSize();
int selectedNode = 0;
if (totalSize <= 1)
{
selectedNode = 0;
}
else
{
if (_nodeSelectionMode.Equals("unbiased"))
{
selectedNode = Rng.Next(totalSize);
}
else
{
if (_nodeSelectionMode.Equals("leaf-probability"))
{
// TODO Implement. Currently runs unbiased
// note: if there aren't any internal nodes, must select leaf, and
// if no leaf, must select internal
selectedNode = Rng.Next(totalSize);
}
else
{
// size-tournament
int maxSize = -1;
selectedNode = 0;
for (int j = 0; j < _nodeSelectionTournamentSize; j++)
{
int nextwhich = Rng.Next(totalSize);
int nextwhichsize = inInd._program.SubtreeSize(nextwhich);
if (nextwhichsize > maxSize)
{
selectedNode = nextwhich;
maxSize = nextwhichsize;
}
}
}
}
}
return(selectedNode);
}
protected internal override string FinalReport()
{
string report = string.Empty;
report += base.FinalReport();
if (!_targetFunctionString.Equals(string.Empty))
{
report += ">> Target Function: " + _targetFunctionString + "\n\n";
}
PushGPIndividual simplified = Autosimplify((PushGPIndividual)_populations[_currentPopulation][_bestIndividual], _finalSimplifications);
// Note: The number of evaluations here will likely be higher than that
// given during the last generational report, since evaluations made
// during simplification count towards the total number of
// simplifications.
report += ">> Number of Evaluations: " + _interpreter.GetEvaluationExecutions() + "\n";
report += ">> Best Program: " + _populations[_currentPopulation][_bestIndividual] + "\n";
report += ">> Fitness (mean): " + _bestMeanFitness + "\n";
if (_testCases.Count == _bestErrors.Count)
{
report += ">> Errors: (";
for (int i = 0; i < _testCases.Count; i++)
{
if (i != 0)
{
report += " ";
}
report += "(" + _testCases[i]._input + " ";
report += Math.Abs(_bestErrors[i]) + ")";
}
report += ")\n";
}
report += ">> Size: " + _bestSize + "\n\n";
report += "<<<<<<<<<< After Simplification >>>>>>>>>>\n";
report += ">> Best Program: ";
report += simplified._program + "\n";
report += ">> Size: ";
report += simplified._program.ProgramSize() + "\n\n";
return(report);
}
protected internal override GAIndividual ReproduceByCrossover(int inIndex)
{
PushGPIndividual a = (PushGPIndividual)ReproduceByClone(inIndex);
PushGPIndividual b = (PushGPIndividual)TournamentSelect(_tournamentSize, inIndex);
if (a._program.ProgramSize() <= 0)
{
return(b);
}
if (b._program.ProgramSize() <= 0)
{
return(a);
}
int aindex = ReproductionNodeSelection(a);
int bindex = ReproductionNodeSelection(b);
if (a._program.ProgramSize() + b._program.SubtreeSize(bindex) - a._program.SubtreeSize(aindex) <= _maxPointsInProgram)
{
a._program.ReplaceSubtree(aindex, b._program.Subtree(bindex));
}
return(a);
}
protected internal override string Report()
{
string report = base.Report();
if (double.IsInfinity(_populationMeanFitness))
{
_populationMeanFitness = double.MaxValue;
}
report += ";; Best Program:\n " + _populations[_currentPopulation][_bestIndividual] + "\n\n";
report += ";; Best Program Fitness (mean): " + _bestMeanFitness + "\n";
if (_testCases.Count == _bestErrors.Count)
{
report += ";; Best Program Errors: (";
for (int i = 0; i < _testCases.Count; i++)
{
if (i != 0)
{
report += " ";
}
report += "(" + _testCases[i]._input + " ";
report += Math.Abs(_bestErrors[i]) + ")";
}
report += ")\n";
}
report += ";; Best Program Size: " + _bestSize + "\n\n";
report += ";; Mean Fitness: " + _populationMeanFitness + "\n";
report += ";; Mean Program Size: " + _averageSize + "\n";
PushGPIndividual simplified = Autosimplify((PushGPIndividual)_populations[_currentPopulation][_bestIndividual], _reportSimplifications);
report += ";; Number of Evaluations Thus Far: " + _interpreter.GetEvaluationExecutions() + "\n";
// string mem = (Runtime.GetRuntime().TotalMemory() / 10000000.0f).ToString();
// report += ";; Memory usage: " + mem + "\n\n";
report += ";; Partial Simplification (may beat best):\n ";
report += simplified._program + "\n";
report += ";; Partial Simplification Size: ";
report += simplified._program.ProgramSize() + "\n\n";
return(report);
}
