public int Compare(IIndividual x1, IIndividual x2)
{
var result = _fitnessFunction.GetValue(x1).CompareTo(_fitnessFunction.GetValue(x2));
if (OptimizationMode == EOptimizationMode.Maximize)
{
result *= -1;
}
return(result);
}
public bool PointSetIsTrunk(PointSet pointSet)
{
if (pointSet == null)
{
return(false);
}
var bestPossibleParameters = FindBestParameters(pointSet);
if (bestPossibleParameters is null)
{
return(false);
}
var fitness = _fitnessFunction.GetValue(bestPossibleParameters.Phenotype);
var isTrunk = fitness <= GetTrunkThreshold(pointSet.Center.Z);
if (!isTrunk)
{
return(false);
}
foreach (var t in pointSet)
{
t.Intensity = (float)fitness;
}
return(true);
}
private string RetrieveBestIndividual(ISet <string> population, IFitnessFunction fitnessFn)
{
string bestIndividual = null;
double bestSoFarFValue = double.NegativeInfinity;
foreach (string individual in population)
{
var fitnessValue = fitnessFn.GetValue(individual);
if (fitnessValue > bestSoFarFValue)
{
bestIndividual = individual;
bestSoFarFValue = fitnessValue;
}
}
return(bestIndividual);
}
public bool Satisfied(IEvolutionaryPopulation population)
{
if (population == null)
{
throw new PopulationNotInitializedException();
}
if (_fitnessFunction == null)
{
throw new FitnessFunctionNotInitializedException();
}
var comparisonResult = _fitnessFunction.GetValue(population.Best).CompareTo(SufficientResult);
comparisonResult *= population.CompareCriteria.OptimizationMode == EOptimizationMode.Minimize ? -1 : 1;
return(comparisonResult >= 0);
}
private string RandomSelection(ISet <string> population,
IFitnessFunction fitnessFn)
{
string selected = null;
// Determine all of the fitness values
double[] fValues = new double[population.Count];
string[] popArray = population.ToArray();
for (int i = 0; i < popArray.Length; i++)
{
fValues[i] = fitnessFn.GetValue(popArray[i]);
}
// Normalize the fitness values
fValues = Util.Util.Normalize(fValues);
double prob = random.NextDouble();
double totalSoFar = 0.0;
for (int i = 0; i < fValues.Length; i++)
{
// Are at last element so assign by default
// in case there are rounding issues with the normalized values
totalSoFar += fValues[i];
if (prob <= totalSoFar)
{
selected = popArray[i];
break;
}
}
// selected may not have been assigned
// if there was a rounding error in the
// addition of the normalized values (i.e. did not total to 1.0)
if (null == selected)
{
// Assign the last value
selected = popArray[popArray.Length - 1];
}
return(selected);
}
