/// <summary>
/// Runs a random walk of n steps along a landscape defined by a crossover operator and returns its results.
/// </summary>
/// <param name="steps">Number of steps.</param>
/// <param name="searchOperator">Operator defining a neighbourhood.</param>
/// <returns></returns>
public RandomWalk RandomWalk(int steps, CrossoverOperator searchOperator)
{
RandomWalk statistics = new RandomWalk(steps);
Chromosome parent1 = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
Chromosome parent2 = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
bool firstParent = RandomGeneratorThreadSafe.NextBool();
Chromosome parent = (firstParent ? parent1 : parent2), child;
GatherData(parent, 0, statistics);
const int minPopSize = 8, maxPopSize = 15;
int popSize = RandomGeneratorThreadSafe.NextInt(minPopSize, maxPopSize);
Chromosome[] supportPopulation = new Chromosome[popSize];
for (int i = 0; i < popSize; ++i)
{
supportPopulation[i] = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
}
IGene[] childGenes1, childGenes2;
for (int i = 0; i < steps; ++i)
{
searchOperator.Run(parent1.Genes, parent2.Genes, out childGenes1, out childGenes2);
child = ChromosomeFactory.MakeChromosome(Problem, RandomGeneratorThreadSafe.NextBool() ? childGenes1 : childGenes2);
GatherData(child, i, statistics);
parent1 = child;
parent2 = supportPopulation[RandomGeneratorThreadSafe.NextInt(popSize)];
}
return(statistics);
}
public void Initialize(int poolSize)
{
_pool = new List <C>();
_poolSize = poolSize;
for (int i = 0; i < _poolSize; i++)
{
_pool.Add(ChromosomeFactory.CreateChromosome());
}
}
public GeneticNeuronalNetwork(int populationSize, List <Article> products, int score, int percentSelection)
{
_populationSize = populationSize;
_products = products;
_score = score;
_percentSelection = percentSelection;
_chromosomes = new List <Chromosome>();
_factory = new ChromosomeFactory();
}
/// <summary>
/// Runs a random walk of n steps along a landscape defined by a mutation operator and returns its results.
/// </summary>
/// <param name="steps">Number of steps.</param>
/// <param name="searchOperator">Operator defining a neighbourhood.</param>
/// <returns></returns>
public RandomWalk RandomWalk(int steps, MutationOperator searchOperator)
{
RandomWalk statistics = new RandomWalk(steps);
Chromosome currentSolution = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
GatherData(currentSolution, 0, statistics);
for (int i = 1; i < steps; ++i)
{
searchOperator.Run(ref currentSolution);
GatherData(currentSolution, i, statistics);
}
return(statistics);
}
public void ClonePopulation_ChangingCloneDosntChangeOriginal()
{
var population = (new double[] { 1, 1, 1, 1 }).ToPopulation("Chromosomes");
var populationClone = population.Clone();
var fakeChromosome = ChromosomeFactory.CreateChromosome(10, "New");
populationClone.GetChromosomes()[0] = fakeChromosome;
foreach (var chromosome in population.GetChromosomes())
{
Assert.AreNotEqual(fakeChromosome, chromosome);
}
}
public void ChangeHistoryLastGeneration_AlgorithmPopulationNotChanged()
{
var expectedPopulation = new double[] { 1, 1 };
var engine = new TestGeneticSearchEngineBuilder(2, 10, expectedPopulation).IncludeAllHistory().Build();
var fakeChromosome = ChromosomeFactory.CreateChromosome(10, "ChangedChromosome");
var result = engine.Next();
result.History[result.History.Count - 1][0] = fakeChromosome;
result = engine.GetCurrentPopulation();
foreach (var chromosome in result.Population.GetChromosomes())
{
Assert.AreNotEqual(fakeChromosome, chromosome);
}
}
public DeploymentModel Run(DeploymentModel initialDeploymentModel)
{
var initialChromosome = ChromosomeFactory.Create(initialDeploymentModel);
var population = InitialPopulationCreator.CreateInitialPopulation(initialChromosome, MinPopulationSize, MaxPopulationSize);
while (!TerminationCondition.HasReached(population, CurrentState))
{
var parents = SelectionStrategyStrategy.SelectChromosomes(MinPopulationSize, population).ToArray();
var offspring = Cross(parents).ToArray();
offspring = Mutate(offspring).ToArray();
population = ReinsertionStrategy.Reinsert(offspring, population);
foreach (var deployment in population.Deployments)
{
deployment.Fitness = FitnessEvaluator.Evaluate(deployment, Workload);
}
CurrentState.UpdateOnGenerationRan(population);
}
return(population.BestDeployment.ToDeploymentModel());
}
