private void UpdateFitnessOfGeneration(BaseEvaluation <GeneType> CalculatesEvaluation)
{
foreach (var chrmosome in Individuals)
{
chrmosome.Fitness = CalculatesEvaluation.CalculatesEvaluation(chrmosome);
}
}
public void UpdateEvaluation(BaseEvaluation <GeneType> newEvaluation)
{
if (newEvaluation != null)
{
Evaluation = newEvaluation;
}
throw new Exception("the new assessment cannot be null");
}
public Generation <GeneType> GenerateNextGeneration(BaseEvaluation <GeneType> CalculatesEvaluation)
{
#region Validation
if (Environment == null || Individuals == null)
{
throw new NullReferenceException();
}
if (Individuals.Count <= 0)
{
throw new Exception("The number of indiciduals must be greater than zero");
}
#endregion Validation
UpdateFitnessOfGeneration(CalculatesEvaluation);
var newGenration = new Generation <GeneType>(NumberOfIndividuals, NumberOfGenesOfIndividuals);
newGenration.Environment = this.Environment;
newGenration.NumberOfGeneration = this.NumberOfGeneration + 1;
newGenration.Individuals = new List <Chromosome <GeneType> >();
while (newGenration.Individuals.Count < NumberOfIndividuals)
{
var fatherId = GetIdOfChromosomeRoulette();
var motherId = GetIdOfChromosomeRoulette();
ValidateParentsId(fatherId, motherId);
while (fatherId == motherId)
{
motherId = GetIdOfRandomChromosome();
ValidateParentsId(fatherId, motherId);
}
#region CheckIfExecuteCrossover
int randPercentage = Random.Next() % 100;
if (randPercentage <= Environment.RateCrossover)
{
var father = GetChromosome(fatherId);
var mother = GetChromosome(motherId);
newGenration.AddIndividualChromosome(ExecuteCrossOver(father, mother));
newGenration.AddIndividualChromosome(ExecuteCrossOver(mother, father));
}
else
{
newGenration.AddIndividualChromosome(GetChromosome(fatherId));
newGenration.AddIndividualChromosome(GetChromosome(motherId));
}
#endregion CheckIfExecuteCrossover
}
#region ExecuteMutation
int randPercentageMutation = Random.Next() % 100;
if (randPercentageMutation <= Environment.RateMutation)
{
Debug.WriteLine("there was a mutation");
int randomChromossomePosition = Random.Next() % Individuals.Count;
Individuals[randomChromossomePosition].PerformsMutation(Random);
}
#endregion ExecuteMutation
return(newGenration);
}
public GeneticAlgorithm(int numberOfGenerations, BaseEvaluation <GeneType> Evaluation)
{
if (numberOfGenerations <= 0)
{
throw new Exception("The number of the generations it is invalid");
}
if (Evaluation == null)
{
throw new Exception("The Evaluation it's not defined");
}
NumberOfGenerations = numberOfGenerations;
this.Evaluation = Evaluation;
}
public Chromosome <GeneType> GetBestChromosome()
{
if (Population == null || Population.Count == 0)
{
throw new Exception("The genetic algorithm hass not been executed");
}
Chromosome <GeneType> ValueReturn = null;
double Evaluation = double.MinValue;
Population.ForEach(x =>
{
x.Individuals.ForEach(y => {
if (y.Fitness > Evaluation)
{
Evaluation = y.Fitness;
ValueReturn = y;
}
});
});
return(ValueReturn);
}
