public GenoTypeFactory(IEaGeneExpressionParameters eaGeneExpressionParameters, IUniformRandomGenerator uniformRandomGenerator, IParameterTerminalFactory parameterTerminalFactory)
{
EaGeneExpressionParameters = eaGeneExpressionParameters;
UniformRandomGenerator = uniformRandomGenerator;
ParameterTerminalFactory = parameterTerminalFactory;
NumberOfPossibleFunctions = EaGeneExpressionParameters.NumberOfPossibleFunctions - 1;
NumberOfPossibleTerminals = EaGeneExpressionParameters.NumberOfPossibleTerminals - 1;
}
public static List <Individual> PerformCrossOver(List <Individual> population, IUniformRandomGenerator randomGenerator, IGenoTypeCrossoverator crossOverator)
{
var returnObject = new List <Individual>();
do
{
var leftIndex = randomGenerator.GetIntegerRandomNumber(0, population.Count - 1);
var leftIndividual = population[leftIndex];
population.RemoveAt(leftIndex);
Individual rightIndividual = null;
if (population.Count > 0)
{
var rightIndex = randomGenerator.GetIntegerRandomNumber(0, population.Count - 1);
rightIndividual = population[rightIndex];
population.RemoveAt(rightIndex);
}
if (rightIndividual != null)
{
crossOverator.PerformCrossover(ref leftIndividual.GenoType, ref rightIndividual.GenoType);
returnObject.Add((Individual)rightIndividual.Clone());
}
returnObject.Add((Individual)leftIndividual.Clone());
} while (population.Count >= 1);
if (population.Count == 1)
{
returnObject.Add(population[0]);
}
return(returnObject);
}
public GenoTypeCrossoveratorBasic1(IUniformRandomGenerator uniformRandomGenerator, IEaGeneExpressionParameters eaGeneExpressionParameters)
{
UniformRandomGenerator = uniformRandomGenerator;
EaGeneExpressionParameters = eaGeneExpressionParameters;
}
public ParameterTerminalFactory(IEaGeneExpressionParameters eAGeneExpressionParameters, IUniformRandomGenerator uniformRandomGenerator)
{
UniformRandomGenerator = uniformRandomGenerator;
EaGeneExpressionParameters = eAGeneExpressionParameters;
PossibleConstantsCountMinusOne = EaGeneExpressionParameters.PossibleConstants.Count - 1;
}
public GenoTypeMutatorBasic1(IUniformRandomGenerator uniformRandomGenerator, IEaGeneExpressionParameters eaGeneExpressionParameters, IGenoTypeFactory genoTypeFactory)
{
UniformRandomGenerator = uniformRandomGenerator;
EaGeneExpressionParameters = eaGeneExpressionParameters;
GenoTypeFactory = genoTypeFactory;
}
public static List <IObjectiveValues> PerformSelection(int tournamentSize, List <IObjectiveValues> originalObjectiveValues, IUniformRandomGenerator randomGenerator)
{
originalObjectiveValues = originalObjectiveValues.Where(x => x.Invalid == false).ToList();
var populationSize = originalObjectiveValues.Count;
originalObjectiveValues = Nsga2Ranker.Rank(originalObjectiveValues);
originalObjectiveValues = Nsga2Crowder.CalculateCrowdingDistances(originalObjectiveValues);
var newPopulationObjectiveValues = new List <IObjectiveValues>();
do
{
var indices = new List <int>();
var pickedObjectiveValues = new List <IObjectiveValues>();
do
{
var index = randomGenerator.GetIntegerRandomNumber(0, originalObjectiveValues.Count - 1);
if (indices.Contains(index))
{
continue;
}
indices.Add(index);
pickedObjectiveValues.Add(originalObjectiveValues[index]);
} while (indices.Count() < tournamentSize);
pickedObjectiveValues = pickedObjectiveValues.OrderBy(i => i.Rank).ToList();
if (pickedObjectiveValues[0].Rank == pickedObjectiveValues[1].Rank)
{
if (pickedObjectiveValues[1].CrowdingDistance > pickedObjectiveValues[0].CrowdingDistance)
{
newPopulationObjectiveValues.Add(pickedObjectiveValues[1]);
}
else
{
newPopulationObjectiveValues.Add(pickedObjectiveValues[0]);
}
}
else
{
newPopulationObjectiveValues.Add(pickedObjectiveValues[0]);
}
} while (newPopulationObjectiveValues.Count < populationSize);
return(newPopulationObjectiveValues);
}
