public override IEnumerable <IndividualBase> Execute()
{
//Initialize Population + Evaluate Objective Values
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
// Assign Rank Based on Pareto Dominance
FastNonDominatedSort(Population);
// Assign Crowding Distance for the Population
CrowdingDistanceAssignment(Population.Content);
// Creates Offspring through Selection, Recombination, and Mutation
Population_MultiObjective_AG childPopulation = Procreate(Population);
Population_MultiObjective_AG Pnext = Population;
Population_MultiObjective_AG Qnext = childPopulation;
Population_MultiObjective_AG currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
for (int idxGeracao = 1; idxGeracao < NumberOfGenerations; ++idxGeracao)
{
Pnext = RunGeneration(currentGeneration);
Qnext = Procreate(Pnext);
currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
}
return(FastNonDominatedSort(currentGeneration).First().Content);
}
public override IEnumerable<IndividualBase> Execute()
{
//Initialize Population + Evaluate Objective Values
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
// Assign Rank Based on Pareto Dominance
FastNonDominatedSort(Population);
// Assign Crowding Distance for the Population
CrowdingDistanceAssignment(Population.Content);
// Creates Offspring through Selection, Recombination, and Mutation
Population_MultiObjective_AG childPopulation = Procreate(Population);
Population_MultiObjective_AG Pnext = Population;
Population_MultiObjective_AG Qnext = childPopulation;
Population_MultiObjective_AG currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
for (int idxGeracao = 1; idxGeracao < NumberOfGenerations; ++idxGeracao) {
Pnext = RunGeneration(currentGeneration);
Qnext = Procreate(Pnext);
currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
}
return FastNonDominatedSort(currentGeneration).First().Content;
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
List <Population_MultiObjective_AG> fronts = FastNonDominatedSort(currentGeneration);
Population_MultiObjective_AG Pnext = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
int i = 0;
while ((Pnext.IndividualCount + fronts[i].IndividualCount) < InitialPopulationSize)
{
CrowdingDistanceAssignment(fronts[i].Content);
Pnext.AddPopulation(fronts[i]);
i++;
}
fronts[i].Content.Sort(CrowdedComparison);
fronts[i].Content.Reverse();
Pnext.AddRange(fronts[i].Content.Take(InitialPopulationSize - Pnext.IndividualCount));
return(Pnext);
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
Population_MultiObjective_AG everyone = new Population_MultiObjective_AG(Problem, InitialPopulationSize + ArchivePopulationSize);
everyone.AddPopulation(currentGeneration);
everyone.AddPopulation(Archive);
#region Fitness Assignment
// The Strength of an Individual is the Count of those he Dominates
foreach (IndividualBase individual in everyone.Content) {
individual.Strength = everyone.Content.Where(I => !I.Equals(individual)).Count(I => individual.Dominates(I));
}
// The RawFitness of an Individual is the sum of the strengths of all those who Dominate him
foreach (IndividualBase individual in everyone.Content) {
individual.RawFitness = everyone.Content.Where(I => I.Dominates(individual)).Sum(D => D.Strength);
}
// The True Fitness is Calculated
everyone.CalculateSortedDistances();
int k = (int)Math.Sqrt(InitialPopulationSize + ArchivePopulationSize);
foreach (IndividualBase i in everyone.Content) {
double distanceSought = i.DistancesToOtherIndividuals[k].Item2;
double density = (1 / (distanceSought + 2));
i.FinalFitness = i.RawFitness + density;
}
#endregion
#region Environment Selection
Population_MultiObjective_AG nextArchive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
List<IndividualBase> excludeList = new List<IndividualBase>();
//insere em nextArchive todos os elementos da populacao total com finalFitness < 1
//poderia ser tambem rawFitness = 0, mas no artigo especifica o dito acima, podemos seguir
//aproveitar pra remover do total os elementos inseridos, vai facilitar no proximo passo..
IEnumerable<IndividualBase> nonDominatedSolutions = everyone.Content.Where(I => I.FinalFitness < 1);
nextArchive.AddRange(nonDominatedSolutions);
everyone.Content.RemoveAll(I => nonDominatedSolutions.Contains(I));
if (nextArchive.IndividualCount < ArchivePopulationSize) {
everyone.Content.Sort(finalFitnessComparison);
//TODO: Confirmar se é melhor maior ou menor, esse reverse faz pegar os menores
everyone.Content.Reverse();
IEnumerable<IndividualBase> complement = everyone.Content.Take(ArchivePopulationSize - nextArchive.IndividualCount);
nextArchive.AddRange(complement);
}
else if(nextArchive.IndividualCount > ArchivePopulationSize) {
//trunca a população pela distancia
nextArchive.Truncate(ArchivePopulationSize);
}
//acho que não vai precisar do antigo mais.. conferir !
this.Archive = nextArchive;
#endregion
#region Mating Selection
//Seleção dos Pais da proxima populacao, leva em conta apenas archive
Population_MultiObjective_AG matingPool = Procreate(nextArchive);
#endregion
return matingPool;
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
List<Population_MultiObjective_AG> fronts = FastNonDominatedSort(currentGeneration);
Population_MultiObjective_AG Pnext = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
int i = 0;
while ((Pnext.IndividualCount + fronts[i].IndividualCount) < InitialPopulationSize) {
CrowdingDistanceAssignment(fronts[i].Content);
Pnext.AddPopulation(fronts[i]);
i++;
}
fronts[i].Content.Sort(CrowdedComparison);
fronts[i].Content.Reverse();
Pnext.AddRange(fronts[i].Content.Take(InitialPopulationSize - Pnext.IndividualCount));
return Pnext;
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
Population_MultiObjective_AG everyone = new Population_MultiObjective_AG(Problem, InitialPopulationSize + ArchivePopulationSize);
everyone.AddPopulation(currentGeneration);
everyone.AddPopulation(Archive);
#region Fitness Assignment
// The Strength of an Individual is the Count of those he Dominates
foreach (IndividualBase individual in everyone.Content)
{
individual.Strength = everyone.Content.Where(I => !I.Equals(individual)).Count(I => individual.Dominates(I));
}
// The RawFitness of an Individual is the sum of the strengths of all those who Dominate him
foreach (IndividualBase individual in everyone.Content)
{
individual.RawFitness = everyone.Content.Where(I => I.Dominates(individual)).Sum(D => D.Strength);
}
// The True Fitness is Calculated
everyone.CalculateSortedDistances();
int k = (int)Math.Sqrt(InitialPopulationSize + ArchivePopulationSize);
foreach (IndividualBase i in everyone.Content)
{
double distanceSought = i.DistancesToOtherIndividuals[k].Item2;
double density = (1 / (distanceSought + 2));
i.FinalFitness = i.RawFitness + density;
}
#endregion
#region Environment Selection
Population_MultiObjective_AG nextArchive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
List <IndividualBase> excludeList = new List <IndividualBase>();
//insere em nextArchive todos os elementos da populacao total com finalFitness < 1
//poderia ser tambem rawFitness = 0, mas no artigo especifica o dito acima, podemos seguir
//aproveitar pra remover do total os elementos inseridos, vai facilitar no proximo passo..
IEnumerable <IndividualBase> nonDominatedSolutions = everyone.Content.Where(I => I.FinalFitness < 1);
nextArchive.AddRange(nonDominatedSolutions);
everyone.Content.RemoveAll(I => nonDominatedSolutions.Contains(I));
if (nextArchive.IndividualCount < ArchivePopulationSize)
{
everyone.Content.Sort(finalFitnessComparison);
//TODO: Confirmar se é melhor maior ou menor, esse reverse faz pegar os menores
everyone.Content.Reverse();
IEnumerable <IndividualBase> complement = everyone.Content.Take(ArchivePopulationSize - nextArchive.IndividualCount);
nextArchive.AddRange(complement);
}
else if (nextArchive.IndividualCount > ArchivePopulationSize)
{
//trunca a população pela distancia
nextArchive.Truncate(ArchivePopulationSize);
}
//acho que não vai precisar do antigo mais.. conferir !
this.Archive = nextArchive;
#endregion
#region Mating Selection
//Seleção dos Pais da proxima populacao, leva em conta apenas archive
Population_MultiObjective_AG matingPool = Procreate(nextArchive);
#endregion
return(matingPool);
}
