/// <summary>
/// Mutates an individual by choosing an index at random and randomizing
/// its training point among possible individuals.
/// </summary>
protected internal override GAIndividual ReproduceByMutation(int inIndex)
{
FloatRegFitPredictionIndividual i = (FloatRegFitPredictionIndividual)ReproduceByClone(inIndex);
int index = Rng.Next(FloatRegFitPredictionIndividual._sampleSize);
i.SetSampleIndex(index, Rng.Next(_solutionGA._testCases.Count));
return(i);
}
/// <summary>Evaluates a solution individual using the best predictor so far.</summary>
protected internal virtual void PredictIndividual(GAIndividual inIndividual, bool duringSimplify)
{
FloatRegFitPredictionIndividual predictor = (FloatRegFitPredictionIndividual)_predictorGA.GetBestPredictor();
float fitness = predictor.PredictSolutionFitness((PushGPIndividual)inIndividual);
inIndividual.SetFitness(fitness);
inIndividual.SetErrors(new List <float>());
}
protected internal override void InitIndividual(GAIndividual inIndividual)
{
FloatRegFitPredictionIndividual i = (FloatRegFitPredictionIndividual)inIndividual;
int[] samples = new int[FloatRegFitPredictionIndividual._sampleSize];
for (int j = 0; j < samples.Length; j++)
{
samples[j] = Rng.Next(_solutionGA._testCases.Count);
}
i.SetSampleIndicesAndSolutionGA(_solutionGA, samples);
}
protected internal override GAIndividual ReproduceByCrossover(int inIndex)
{
FloatRegFitPredictionIndividual a = (FloatRegFitPredictionIndividual)ReproduceByClone(inIndex);
FloatRegFitPredictionIndividual b = (FloatRegFitPredictionIndividual)TournamentSelect(_tournamentSize, inIndex);
// crossoverPoint is the first index of a that will be changed to the
// gene from b.
int crossoverPoint = Rng.Next(FloatRegFitPredictionIndividual._sampleSize - 1) + 1;
for (int i = crossoverPoint; i < FloatRegFitPredictionIndividual._sampleSize; i++)
{
a.SetSampleIndex(i, b.GetSampleIndex(i));
}
return(a);
}
protected internal override void EvaluateIndividual(GAIndividual inIndividual)
{
FloatRegFitPredictionIndividual predictor = (FloatRegFitPredictionIndividual)inIndividual;
List <float> errors = new List <float>();
for (int i = 0; i < _trainerPopulationSize; i++)
{
float predictedError = predictor.PredictSolutionFitness(_trainerPopulation[i]);
// Error is difference between predictedError and the actual fitness
// of the trainer.
float error = Math.Abs(predictedError) - Math.Abs(_trainerPopulation[i].GetFitness());
errors.Add(error);
}
predictor.SetFitness(AbsoluteAverageOfErrors(errors));
predictor.SetErrors(errors);
}
