public void RealiseEvolution()
{
// Initialisation
ReferenceList referenceList = new ReferenceList("tsp.riesenia");
Initialisation initialisation = new Initialisation(initialPopulationCount, referenceList);
List <Individual> population = initialisation.InitialisePopulation();
// Evaluation
Evaluation evaluation = new Evaluation(referenceList);
foreach (Individual individual in population)
{
evaluation.EvaluateIndividual(individual);
}
// Validation
foreach (Individual individual in population)
{
if (StaticOperations.ValidateIndividual(individual) == false)
{
throw new NotSupportedException();
}
}
// Evolution cycles
for (int i = 0; i < evolutionCycles; i++)
{
Console.Write("Epoch #" + i);
// Selection
Selection selection = new Selection(population, population.Count);
List <Individual> parents = selection.SelectParents(2);
// Genetic operators
GeneticOperators geneticOperators = new GeneticOperators();
List <Individual> descendants = new List <Individual>();
for (int j = 0; j < parents.Count; j = j + 2)
{
descendants.AddRange(geneticOperators.GenerateDescendants(parents[j], parents[j + 1]));
}
// Validation
foreach (Individual individual in descendants)
{
if (StaticOperations.ValidateIndividual(individual) == false)
{
throw new NotSupportedException();
}
}
// Evaluation
foreach (Individual individual in descendants)
{
evaluation.EvaluateIndividual(individual);
}
// Replacement
Replacement replacement = new Replacement(population, descendants, population.Count);
population = replacement.NextGeneration();
// Save best individual
List <Individual> orderedPopulation = population.OrderBy(item => item.Fitness).ToList();
bestIndividualPerGeneration.Add(orderedPopulation[0]);
Console.WriteLine(" Minimum fitness: " + orderedPopulation[0].Fitness);
}
SaveBestIndividualsToFile(referenceList);
}
public void RealiseEvolution()
{
// Initialise population
Initialisation initialisation = new Initialisation(initialPopulationCount, goldFieldCount);
List <Individual> population = initialisation.InitialisePopulation();
// Validate population
for (int i = 0; i < population.Count; i++)
{
if (StaticOperations.ValidateIndividual(population[i]) == false)
{
throw new NotSupportedException();
}
}
// Evaluate population
Evaluation evaluation = new Evaluation();
for (int i = 0; i < population.Count; i++)
{
evaluation.EvaluateIndividual(population[i]);
}
// Evolution cycle
for (int i = 0; i < evolutionCycles; i++)
{
Console.Write("# Epoch " + (i + 1));
// Selection
Selection selection = new Selection(population, population.Count);
// Q tournament
List <Individual> parents = selection.SelectParents(4);
// Genetic operators
List <Individual> descendants = new List <Individual>();
GeneticOperators geneticOperators = new GeneticOperators();
for (int j = 0; j < parents.Count; j = j + 2)
{
descendants.AddRange(geneticOperators.GenerateDescendants(parents[j], parents[j + 1]));
}
// Evaluation
for (int j = 0; j < descendants.Count; j++)
{
evaluation.EvaluateIndividual(descendants[j]);
}
// Replacement
Replacement replacement = new Replacement(population, descendants, population.Count);
if (i - bestFitnessEpoch < 100)
{
population = replacement.NextGeneration();
}
else
{
population = replacement.KillBestIndividuals();
bestFitness = double.MaxValue;
}
foreach (Individual individual in population)
{
if (StaticOperations.ValidateIndividual(individual) == false)
{
throw new NotSupportedException();
}
}
// Save best member
List <Individual> orderedPopulation = population.OrderBy(ind => ind.Fitness).ToList();
bestIndividualsPerGeneration.Add(orderedPopulation[0]);
Console.WriteLine(" Minimum fitness: " + orderedPopulation[0].Fitness + ".");
if (orderedPopulation[0].Fitness < bestFitness)
{
bestFitness = orderedPopulation[0].Fitness;
bestFitnessEpoch = i;
}
if (orderedPopulation[0].Fitness == 0)
{
break;
}
}
SaveDataToFile();
}
