private static void AddCrossoverMethodExperiment(ExperimentSet newSet, ICrossoverMethod crossoverMethod, string title)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.CrossoverMethod = crossoverMethod;
newExperiment.Label = "Crossover Method: " + title;
}
public void Start()
{
//FilePath = "C:\\Users\\JKMT\\Desktop\\OptimizationShooting.txt";
leftRules = new List <EvaluationTreeRuleBase>(PopulationSize);
rightRules = new List <EvaluationTreeRuleBase>(PopulationSize);
for (int i = 0; i < PopulationSize; i++)
{
leftRules.Add(FuzzyShootingUtil.CreateRandomEvaluationTreeRuleBase());
rightRules.Add(FuzzyShootingUtil.CreateRandomEvaluationTreeRuleBase());
}
IsTrialRunning = false;
TrialCount = 0;
GenerationCount = 1;
TrialController = Trial.GetComponent <TrialController>();
OrganismFactory = new EvaluationTreeRuleBaseFactory(FuzzyShootingUtil.InputSets, FuzzyShootingUtil.TorqueSet);
GeneCopier = new RuleCopier();
SelectionMethod = new StochasticUniversalSamplingSelection(0.11f, 0.29f);
CrossoverMethod = new UniformCrossover();
GeneticAlgorithm = new GeneticAlgorithm <EvaluationTreeRuleBase, Rule>(SelectionMethod, CrossoverMethod, OrganismFactory, GeneCopier);
}
private static void AddCrossoverMethodExperiment(ExperimentSet newSet, ICrossoverMethod crossoverMethod, string title)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.CrossoverMethod = crossoverMethod;
newExperiment.Label = "Crossover Method: " + title;
}
public Population(IFitnessFunction fitnessFunction, List <IGenome> population)
{
//Log.Create("../../Logs/");
this.selection = DefaultParameter.selection;
this.crossover = DefaultParameter.crossover;
this.mutation = DefaultParameter.mutation;
this.population = population;
}
public Population(IFitnessFunction fitnessFunction, List<IGenome> population)
{
//Log.Create("../../Logs/");
this.selection = DefaultParameter.selection;
this.crossover = DefaultParameter.crossover;
this.mutation = DefaultParameter.mutation;
this.population = population;
}
public Population(IFitnessFunction fitnessFunction, int size)
{
// Log.Create("../../Logs/");
this.selection = DefaultParameter.selection;
this.crossover = DefaultParameter.crossover;
this.mutation = DefaultParameter.mutation;
this.generation = 1;
this.avarageFitness = 0;
this.fitnessFunction = fitnessFunction;
IInitialPopulationMethod initial = DefaultParameter.initialPopulation;
this.population = initial.Generate(DefaultParameter.genomeSize, fitnessFunction);
}
public Population(IFitnessFunction fitnessFunction, int size)
{
// Log.Create("../../Logs/");
this.selection = DefaultParameter.selection;
this.crossover = DefaultParameter.crossover;
this.mutation = DefaultParameter.mutation;
this.generation = 1;
this.avarageFitness = 0;
this.fitnessFunction = fitnessFunction;
IInitialPopulationMethod initial = DefaultParameter.initialPopulation;
this.population = initial.Generate(DefaultParameter.genomeSize, fitnessFunction);
}
/// <summary>
/// Dos a crossover between this IChromosome and another by applying the specified ICrossoverMethod.
/// </summary>
/// <returns>The resulting offspring.</returns>
/// <param name="crossoverMethod">Crossover method.</param>
/// <param name="other">Other.</param>
public IChromosome <T> DoCrossover(ICrossoverMethod crossoverMethod, IChromosome <T> other)
{
if (Length != other.Length)
{
throw new ArgumentException("Chromosomes must be of same length");
}
var childGenes = new T[Length];
for (var i = 0; i < Length; i++)
{
childGenes[i] = crossoverMethod.SelectGene(i, this, other);
}
return(new Chromosome <T>(childGenes));
}