public void RegisterNewGeneration_GenerationExceedGenerationsNumber_RemoveOldOne()
{
var target = new PerformanceGenerationStrategy();
var population = new Population(2, 6, new ChromosomeStub());
population.CreateInitialGeneration();
target.RegisterNewGeneration(population);
Assert.AreEqual(1, population.Generations.Count);
population.CreateNewGeneration(new List <IChromosome>()
{
new ChromosomeStub(), new ChromosomeStub()
});
target.RegisterNewGeneration(population);
Assert.AreEqual(1, population.Generations.Count);
population.CreateNewGeneration(new List <IChromosome>()
{
new ChromosomeStub(), new ChromosomeStub()
});
target.RegisterNewGeneration(population);
Assert.AreEqual(1, population.Generations.Count);
population.CreateNewGeneration(new List <IChromosome>()
{
new ChromosomeStub(), new ChromosomeStub()
});
target.RegisterNewGeneration(population);
Assert.AreEqual(1, population.Generations.Count);
}
public static Population GeneratePopulation(int size, object chromosome)
{
var defaultPerformance = new PerformanceGenerationStrategy(10);
if (chromosome is BinaryChromosome)
{
return(GeneratePopulation(size, (BinaryChromosome)chromosome, defaultPerformance));
}
if (chromosome is CombinatorialChromosome)
{
return(GeneratePopulation(size, (CombinatorialChromosome)chromosome, defaultPerformance));
}
if (chromosome is DoubleChromosome)
{
return(GeneratePopulation(size, (DoubleChromosome)chromosome, defaultPerformance));
}
return(null);
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.Populations.Population"/> class.
/// </summary>
/// <param name="minSize">The minimum size (chromosomes).</param>
/// <param name="maxSize">The maximum size (chromosomes).</param>
/// <param name="adamChromosome">The original chromosome of all population ;).</param>
public GeneticDietPopulation(int minSize, int maxSize, IChromosome adamChromosome)
{
if (minSize < 2)
{
throw new ArgumentOutOfRangeException("minSize", "The minimum size for a population is 2 chromosomes.");
}
if (maxSize < minSize)
{
throw new ArgumentOutOfRangeException("maxSize", "The maximum size for a population should be equal or greater than minimum size.");
}
ExceptionHelper.ThrowIfNull("adamChromosome", adamChromosome);
CreationDate = DateTime.Now;
MinSize = minSize;
MaxSize = maxSize;
AdamChromosome = adamChromosome;
Generations = new List <Generation>();
GenerationStrategy = new PerformanceGenerationStrategy(10);
}
public Test UseAlgorithm(List <int> oblasti, List <double> zastupljenost, int Test_Length)
{
Random r = new Random(10);
IDataAccess dataAccess = DataAccess.DataAccess.GetInstance();
List <Question> qs = new List <Question>();
for (int i = 0; i < Test_Length; i++)
{
qs.Add(dataAccess.GetQuestionById(1 + r.Next(1000)));
}
Test adamTest = new Test(qs, Test_Length);
List <double> zastupljenost_kao_br_pitanja = new List <double>();
foreach (var vrednost in zastupljenost)
{
zastupljenost_kao_br_pitanja.Add(Math.Round(vrednost * Test_Length + 0.01));
}
var fitness = new TestFitness(oblasti, zastupljenost_kao_br_pitanja, 0);
IGenerationStrategy generationStrategy = new PerformanceGenerationStrategy(3);
var population = new TestPopulation(20, 50, adamTest, oblasti)
{
GenerationStrategy = generationStrategy
};
ISelection selection = new EliteSelection();
ICrossover crossover = new TestCrossover(oblasti, zastupljenost_kao_br_pitanja, 8, 4);
IMutation mutation = new TestMutation(oblasti, zastupljenost_kao_br_pitanja);
ITermination termination = new OrTermination(new ITermination[] { new TestTermination(100, oblasti),
new FitnessThresholdTermination(oblasti.Count),
new GenerationNumberTermination(3000) });
GeneticAlgorithm ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination,
CrossoverProbability = 1f,
MutationProbability = 0.7f
};
double oldFitness = 0;
List <Test> tests = new List <Test>();
//Callback
ga.GenerationRan += (sender, arg) =>
{
if (ga.BestChromosome.Fitness >= oldFitness)
{
oldFitness = ga.BestChromosome.Fitness.Value;
tests.Add(ga.BestChromosome as Test);
//Console.WriteLine($"Current fitness: {ga.BestChromosome.Fitness} for generation : {ga.GenerationsNumber}");
}
};
ga.Start();
if (oldFitness == 0)
{
return(null);
}
var lista = tests.Where(x => !x.HasDuplicate()).OrderByDescending(x => x.Fitness.Value).ToList();
Test bestChromosome = lista.FirstOrDefault();
foreach (var q in bestChromosome.questions)
{
q.Izabrano = true;
}
return(bestChromosome);
}
public static PerformanceGenerationStrategy PerformanceStrategy(int maxGenerations = 10)
{
PerformanceGenerationStrategy strategy = new PerformanceGenerationStrategy(maxGenerations);
return(strategy);
}
