/// <summary>
/// Resize population to the new specified size.
/// </summary>
///
/// <param name="newPopulationSize">New size of population.</param>
/// <param name="membersSelector">Selection algorithm to use in the case
/// if population should get smaller.</param>
///
/// <remarks><para>The method does resizing of population. In the case if population
/// should grow, it just adds missing number of random members. In the case if
/// population should get smaller, the specified selection method is used to
/// reduce the population.</para></remarks>
///
/// <exception cref="ArgumentException">Too small population's size was specified. The
/// exception is thrown in the case if <paramref name="newPopulationSize"/> is smaller than 2.</exception>
///
public void Resize(int newPopulationSize, ISelectionMethod membersSelector)
{
if (newPopulationSize < 2)
{
throw new ArgumentException("Too small new population's size was specified.");
}
if (newPopulationSize > size)
{
// population is growing, so add new rundom members
// Note: we use population.Count here instead of "size" because
// population may be bigger already after crossover/mutation. So
// we just keep those members instead of adding random member.
var toAdd = newPopulationSize - population.Count;
for (var i = 0; i < toAdd; i++)
{
// create new chromosome
var c = population[0].CreateNew();
// calculate it's fitness
c.Evaluate(fitnessFunction);
// add it to population
population.Add(c);
}
}
else
{
// do selection
membersSelector.ApplySelection(population, newPopulationSize);
}
size = newPopulationSize;
}
/// <summary>
/// Do selection.
/// </summary>
///
/// <remarks>The method applies selection operator to the current population. Using
/// specified selection algorithm it selects members to the new generation from current
/// generates and adds certain amount of random members, if is required
/// (see <see cref="RandomSelectionPortion"/>).</remarks>
///
public virtual void Selection()
{
// amount of random chromosomes in the new population
var randomAmount = (int)(randomSelectionPortion * size);
// do selection
selectionMethod.ApplySelection(population, size - randomAmount);
// add random chromosomes
if (randomAmount > 0)
{
var ancestor = population[0];
for (var i = 0; i < randomAmount; i++)
{
// create new chromosome
var c = ancestor.CreateNew();
// calculate it's fitness
c.Evaluate(fitnessFunction);
// add it to population
population.Add(c);
}
}
FindBestChromosome();
}
#pragma warning disable CS0436 // Type conflicts with imported type
/// <summary>
/// Perform migration between two populations.
/// </summary>
///
/// <param name="anotherPopulation">Population to do migration with.</param>
/// <param name="numberOfMigrants">Number of chromosomes from each population to migrate.</param>
/// <param name="migrantsSelector">Selection algorithm used to select chromosomes to migrate.</param>
///
/// <remarks><para>The method performs migration between two populations - current and the
/// <paramref name="anotherPopulation">specified one</paramref>. During migration
/// <paramref name="numberOfMigrants">specified number</paramref> of chromosomes is choosen from
/// each population using <paramref name="migrantsSelector">specified selection algorithms</paramref>
/// and put into another population replacing worst members there.</para></remarks>
///
public void Migrate(Population anotherPopulation, int numberOfMigrants, ISelectionMethod migrantsSelector)
#pragma warning restore CS0436 // Type conflicts with imported type
{
int currentSize = this.size;
int anotherSize = anotherPopulation.Size;
// create copy of current population
List <IChromosome> currentCopy = new List <IChromosome>( );
for (int i = 0; i < currentSize; i++)
{
currentCopy.Add(population[i].Clone( ));
}
// create copy of another population
List <IChromosome> anotherCopy = new List <IChromosome>( );
for (int i = 0; i < anotherSize; i++)
{
anotherCopy.Add(anotherPopulation.population[i].Clone( ));
}
// apply selection to both populations' copies - select members to migrate
migrantsSelector.ApplySelection(currentCopy, numberOfMigrants);
migrantsSelector.ApplySelection(anotherCopy, numberOfMigrants);
// sort original populations, so the best chromosomes are in the beginning
population.Sort( );
anotherPopulation.population.Sort( );
// remove worst chromosomes from both populations to free space for new members
population.RemoveRange(currentSize - numberOfMigrants, numberOfMigrants);
anotherPopulation.population.RemoveRange(anotherSize - numberOfMigrants, numberOfMigrants);
// put migrants to corresponding populations
population.AddRange(anotherCopy);
anotherPopulation.population.AddRange(currentCopy);
// find best chromosomes in each population
FindBestChromosome( );
anotherPopulation.FindBestChromosome( );
}
public void pruebaSeleccion()
{
for (int i = 0; i < 100; i++)
{
var cromosoma = new CocheCromosoma();
cromosoma.Generate();
cromosoma.Evaluate(fitness);
cromosomas.Add(cromosoma);
}
seleccion = new CocheSeleccion();
Console.WriteLine(this.cromosomas.Count);
seleccion.ApplySelection(this.cromosomas, 20);
Console.WriteLine(this.cromosomas.Count);
}
/// <summary>
/// Resize population to the new specified size.
/// </summary>
///
/// <param name="newPopulationSize">New size of population.</param>
/// <param name="membersSelector">Selection algorithm to use in the case
/// if population should get smaller.</param>
///
/// <remarks><para>The method does resizing of population. In the case if population
/// should grow, it just adds missing number of random members. In the case if
/// population should get smaller, the specified selection method is used to
/// reduce the population.</para></remarks>
///
/// <exception cref="ArgumentException">Too small population's size was specified. The
/// exception is thrown in the case if <paramref name="newPopulationSize"/> is smaller than 2.</exception>
///
public void Resize(int newPopulationSize, ISelectionMethod membersSelector)
{
if (newPopulationSize < 2)
throw new ArgumentException("Too small new population's size was specified.");
if (newPopulationSize > size)
{
// population is growing, so add new rundom members
// Note: we use population.Count here instead of "size" because
// population may be bigger already after crossover/mutation. So
// we just keep those members instead of adding random member.
int toAdd = newPopulationSize - population.Count;
for (int i = 0; i < toAdd; i++)
{
// create new chromosome
IChromosome c = population[0].CreateNew();
// calculate it's fitness
c.Evaluate(fitnessFunction);
// add it to population
population.Add(c);
}
}
else
{
// do selection
membersSelector.ApplySelection(population, newPopulationSize);
}
size = newPopulationSize;
}
/// <summary>
/// Perform migration between two populations.
/// </summary>
///
/// <param name="anotherPopulation">Population to do migration with.</param>
/// <param name="numberOfMigrants">Number of chromosomes from each population to migrate.</param>
/// <param name="migrantsSelector">Selection algorithm used to select chromosomes to migrate.</param>
///
/// <remarks><para>The method performs migration between two populations - current and the
/// <paramref name="anotherPopulation">specified one</paramref>. During migration
/// <paramref name="numberOfMigrants">specified number</paramref> of chromosomes is choosen from
/// each population using <paramref name="migrantsSelector">specified selection algorithms</paramref>
/// and put into another population replacing worst members there.</para></remarks>
///
public void Migrate(Population anotherPopulation, int numberOfMigrants, ISelectionMethod migrantsSelector)
{
int currentSize = this.size;
int anotherSize = anotherPopulation.Size;
// create copy of current population
List<IChromosome> currentCopy = new List<IChromosome>();
for (int i = 0; i < currentSize; i++)
{
currentCopy.Add(population[i].Clone());
}
// create copy of another population
List<IChromosome> anotherCopy = new List<IChromosome>();
for (int i = 0; i < anotherSize; i++)
{
anotherCopy.Add(anotherPopulation.population[i].Clone());
}
// apply selection to both populations' copies - select members to migrate
migrantsSelector.ApplySelection(currentCopy, numberOfMigrants);
migrantsSelector.ApplySelection(anotherCopy, numberOfMigrants);
// sort original populations, so the best chromosomes are in the beginning
population.Sort();
anotherPopulation.population.Sort();
// remove worst chromosomes from both populations to free space for new members
population.RemoveRange(currentSize - numberOfMigrants, numberOfMigrants);
anotherPopulation.population.RemoveRange(anotherSize - numberOfMigrants, numberOfMigrants);
// put migrants to corresponding populations
population.AddRange(anotherCopy);
anotherPopulation.population.AddRange(currentCopy);
// find best chromosomes in each population
FindBestChromosome();
anotherPopulation.FindBestChromosome();
}
