public ChartData(GADefinition gaDefinition, AdvancedCollectionView filteringCollection, Guid key)
{
GaDefinition = gaDefinition;
FilteringCollection = filteringCollection;
Key = key;
}
/// <summary>
/// Loads presetsProfile into service.
/// </summary>
/// <exception cref="T:System.InvalidOperationException">Thrown when an attempt to load presetsProfile twice is made.</exception>
public void AddGeneticAlgorithm(GADefinition definition, Guid Key)
{
var chromosome = new FloatingPointChromosome(
minValue: new double[] { 0, 0, 0, 0 },
maxValue: new double[] { _maxWidth, _maxHeight, _maxWidth, _maxHeight },
totalBits: new int[] { 20, 20, 20, 20 },
fractionDigits: new int[] { 0, 0, 0, 0 }, geneValues: _geneValues);
ICrossover gaCrossover = default;
switch (definition.Crossover)
{
case Crossovers.Uniform:
gaCrossover = new UniformCrossover(mixProbability: 0.5f);
break;
case Crossovers.OnePoint:
gaCrossover = new OnePointCrossover(swapPointIndex: 40);
break;
case Crossovers.ThreeParent:
gaCrossover = new ThreeParentCrossover();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Crossover),
actualValue: definition.Crossover, message: "Crossover has wrong value");
}
ISelection gaSelection = default;
switch (definition.Selection)
{
case Selections.Elite:
gaSelection = new EliteSelection();
break;
case Selections.Roulette:
gaSelection = new RouletteWheelSelection();
break;
case Selections.Tournament:
gaSelection = new TournamentSelection(
size: decimal.ToInt32(d: decimal.Multiply(definition.Population, new decimal(0.2f))));
break;
case Selections.StohasticUniversalSampling:
gaSelection = new StochasticUniversalSamplingSelection();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Selection),
actualValue: definition.Selection, message: "Selection has wrong value");
}
var gaMutation = new UniformMutation(true);
var gaPopulation = new Population(minSize: definition.Population,
maxSize: definition.Population, adamChromosome: chromosome);
var ga = new GeneticAlgorithm(population: gaPopulation,
fitness: new EuclideanDistanceFitness(), selection: gaSelection,
crossover: gaCrossover, mutation: gaMutation);
ga.MutationProbability = (float)definition.Mutation;
ga.GenerationRan += GeneticAlgorithmOnGenerationRan;
ga.Termination =
new FitnessStagnationTermination(expectedStagnantGenerationsNumber: 5);
_geneticAlgorithms.Add(key: Key, value: ga);
}
