public SimpleGeneticAlgorithm(
ISelectionAlgorithm selection,
ICrossoverAlgorithm crossover,
IMutationAlgorithm mutation,
IFitnessFunction fitnessFunction)
{
Selection = selection;
Crossover = crossover;
Mutation = mutation;
FitnessFunction = fitnessFunction;
_crossover = new Crossover(crossover);
_mutation = new Mutation(mutation);
}
public GA(
int genomLength,
int populationSize,
int crossoverPointNum,
PointMutationPars mutationPars,
ISelectionAlgorithm selectionAlgorithm,
IComparer<Genom> fitnessComparer) :
base(genomLength, populationSize, mutationPars, fitnessComparer)
{
Contract.Requires(genomLength > 0);
Contract.Requires(populationSize > 1);
Contract.Requires(crossoverPointNum > 0);
Contract.Requires(fitnessComparer != null);
Contract.Requires(selectionAlgorithm != null);
CrossoverPointNum = crossoverPointNum;
this.selectionAlgorithm = selectionAlgorithm;
}
public CrossEntropy(
int genomLength,
int populationSize,
double survivalPercent,
PointMutationPars mutationPars,
ISelectionAlgorithm selectionAlgorithm,
IComparer<Genom> fitnessComparer) :
base(genomLength, populationSize, mutationPars, fitnessComparer)
{
Contract.Requires(genomLength > 0);
Contract.Requires(populationSize > 1);
Contract.Requires(survivalPercent > 0.0 && survivalPercent < 100.0);
Contract.Requires(fitnessComparer != null);
Contract.Requires(selectionAlgorithm != null);
SurvivalPercent = survivalPercent;
this.selectionAlgorithm = selectionAlgorithm;
survivalCount = (int)Math.Round((double)PopulationSize * (SurvivalPercent / 100.0), MidpointRounding.AwayFromZero);
}
public Generation <TSol, TGene, TFitness> SelectNextGeneration(
ISelectionAlgorithm <TSol, TFitness> selectionAlgorithm,
double mutationRate = 0.05)
{
var parents = selectionAlgorithm.SelectParents(SortedPopulation).GetEnumerator();
//Breed from the survivors.
var newChromosomes = new List <TSol>(SortedPopulation.Count);
while (newChromosomes.Count < SortedPopulation.Count)
{
parents.MoveNext();
var parentOne = parents.Current;
parents.MoveNext();
var parentTwo = parents.Current;
newChromosomes.Add(parentOne.CrossWith(parentTwo, mutationRate));
newChromosomes.Add(parentOne);
newChromosomes.Add(parentTwo);
}
return(new Generation <TSol, TGene, TFitness>(newChromosomes));
}
public TextFormatter(ISelectionAlgorithm selection, ITextResource textResource)
{
_selection = selection;
_textResource = textResource;
}
/// <summary>
/// Use the given base selection algorithm for selecting parents from the truncated list
/// </summary>
/// <param name="selectionIntensity">Pick the top fraction. Eg. 0.05 => top 5% </param>
/// <param name="baseSelectionAlgorithm">For selecting from the truncated list</param>
public TruncationSelection(double selectionIntensity, ISelectionAlgorithm <TSol, TFitness> baseSelectionAlgorithm)
{
this.selectionIntensity = selectionIntensity;
this.baseSelectionAlgorithm = baseSelectionAlgorithm;
}
