public MainWindow()
{
InitializeComponent();
RandomNumberMain = new RandomNumber();
ListaOsobnikow = new List <DataVariablesToList>();
ParyOsobnikow = new List <Crossover>();
Krzyzowanie = new Crossover();
header = new DataVariablesToList(0.75);
LoadList();
}
public MainAlgorithm(double crossoverProbability, double mutationProbability, int populationSize,
int generationCount, FirstGeneration firstGeneration, FitnessValue fitnessValue, Crossover crossover,
Mutation mutation)
{
CrossoverProbability = crossoverProbability;
MutationProbability = mutationProbability;
PopulationSize = populationSize;
GenerationCount = generationCount;
_firstGeneration = firstGeneration;
_fitnessValue = fitnessValue;
_crossover = crossover;
_mutation = mutation;
}
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 Genome(DNA[] dnaArray, Mutant mutant, Crossover crossover)
{
this.mutant = mutant;
this.crossover = crossover;
this.dnas = new DNA[0];
Array.Resize <DNA>(ref this.dnas, dnaArray.Length);
int i = 0;
foreach (var dnaOrg in dnaArray)
{
this.dnas[i] = dnaOrg.Clone();
++i;
}
}
public void NewGeneration()
{
int finalCount = Settings.PopulationSize + Settings.NumNewDNA;
List <Chromosome> newPopulation = new List <Chromosome>();
if (finalCount <= 0)
{
return;
}
if (Population.Count > 0)
{
CalculateAllFitness();
Population = Population.OrderBy(chrmsm => chrmsm.Fitness).ToList();
}
for (int i = 0; i < finalCount; i++)
{
// Keep only top individuals of the previous generation
if (i < Elitism && i < Settings.PopulationSize)
{
newPopulation.Add(Population[i]);
//Console.WriteLine("Generation: " + Generation);
//Console.WriteLine("/n");
//Console.WriteLine("genes: [{0}]", string.Join(",\t", Population[i].genes));
//Console.WriteLine(Population[i].fitness);
}
else if (i < Settings.PopulationSize)
{
Chromosome parent1 = Selection.PerformSelection(Population);
Chromosome parent2 = Selection.PerformSelection(Population);
Chromosome child = Crossover.PerformCrossover(parent1, parent2);
child = Mutation.PerformMutation(child);
newPopulation.Add(child);
}
else if (Settings.EnableCrossoverNewDNA)
{
newPopulation.Add(new Chromosome(shouldInitGenes: true));
}
}
Population = newPopulation;
Generation++;
}
public void Epoch()
{
CalcFitness();
var newpopulations = new List <IGenome>();
Population = Population.OrderBy(o => o.Fitness).ToList();
for (int j = 0; j < GASettings.BestSolutionToPick; j++)
{
Population[j].Fitness = Fitness.Calc(Population[j]);
Population[j].Score = 0;
newpopulations.Add(Population[j]);
}
int ran = Rand.Next(1, Population.Count);
while (newpopulations.Count < Population.Count)
{
// Selection
var nodemom = Selection.Select(Population);
var nodedad = Selection.Select(Population);
// CrossOver
var cross = Crossover.Calc(new CrossoverOperation(nodemom, nodedad));
//// Mutation
nodemom = Mutate.Calc(cross.Mom);
nodedad = Mutate.Calc(cross.Dad);
// Adaptation
nodemom = Adaptation.Calc(nodemom);
nodedad = Adaptation.Calc(nodedad);
nodemom.Fitness = Fitness.Calc(nodemom);
nodedad.Fitness = Fitness.Calc(nodedad);
// Add in new population
newpopulations.Add(nodemom);
newpopulations.Add(nodedad);
}
Population = null;
Population = newpopulations.ToList();
Generation++;
}
public virtual void PerformIteration()
{
foreach (var chromosome in Population)
{
chromosome.FittnessValue = FittnessFunction.Calculate(chromosome);
}
var selection = Selection.Select(Population);
var crossover = Crossover.Perform(selection);
foreach (var chromosome in crossover)
{
Mutation.Mutate(chromosome);
}
Population.Remove(Population.FirstOrDefault(
x => x.FittnessValue == Population.Min(y => y.FittnessValue)));
Population.Remove(Population.FirstOrDefault(
x => x.FittnessValue == Population.Min(y => y.FittnessValue)));
Population.AddRange(crossover);
}
public GeneticAlgorithm(Selection <T> selectionMethod, Crossover <T> crossoverMethod, Mutation <T> mutationMethod, FitnessFunction <T> fitnessFunction, CreateNewSolution <T> createNewSolution, AcceptResult <T> acceptResult, int population, double mutationProbability, int selectionCount, int eliteCount = 0)
{
if (population < 2)
{
throw new Exception("Population must be greater or equal 2.");
}
if (eliteCount >= population)
{
throw new Exception("EliteCount must be less than population.");
}
selection = selectionMethod;
crossover = crossoverMethod;
mutation = mutationMethod;
fitness = fitnessFunction;
createSolution = createNewSolution;
accept = acceptResult;
maxPopulation = population;
this.mutationProbability = mutationProbability;
this.selectionCount = selectionCount;
this.eliteCount = eliteCount;
}
public void NextGeneration()
{
Selection.SelectPopulation();
Crossover.CrossPopulation();
Mutation.MutatePopulation();
}
