/// <summary>
/// Creates instance of genetic algorithm
/// </summary>
/// <param name="graph">Graph on which algorithm will operate</param>
/// <param name="problem">Problem for which algorithm will attempt to find solution</param>
/// <param name="geneticOperators">Genetic operators used for breeding new generations</param>
/// <param name="settings">General settings for solution finding process</param>
/// <param name="startingPopulation">Starting population for algorithm</param>
//TODO: Introduce SavedState complex type
public GeneticAlgorithm(Graph graph, IProblem problem, GeneticOperators geneticOperators,
GeneticAlgorithmSettings settings, ICollection <Individual> startingPopulation = null,
uint currentGeneration = 0, Guid?id = null)
: base(graph, problem, id)
{
if (geneticOperators == null)
{
throw new ArgumentNullException(nameof(geneticOperators));
}
if (!geneticOperators.IsValid())
{
throw new ArgumentException("Genetic operators are not valid", nameof(geneticOperators));
}
if (settings == null)
{
throw new ArgumentNullException(nameof(settings));
}
GeneticOperators = geneticOperators;
Settings = settings;
if (startingPopulation == null || startingPopulation.Count == 0 || !startingPopulation.All(IsIndividualValid))
{
GenerateInitialPopulation();
}
else
{
CurrentPopulation = new SortedSet <Individual>();
foreach (var element in startingPopulation)
{
CurrentPopulation.Add(element);
}
}
CurrentGeneration = currentGeneration;
}
/// <summary>
/// Creates instance of genetic algorithm
/// </summary>
/// <param name="graph">Graph on which algorithm will operate</param>
/// <param name="problem">Problem for which algorithm will attempt to find solution</param>
/// <param name="geneticOperators">Genetic operators used for breeding new generations</param>
/// <param name="settings">General settings for solution finding process</param>
/// <param name="startingPopulation">Starting population for algorithm</param>
//TODO: Introduce SavedState complex type
public GeneticAlgorithm(Graph graph, IProblem problem, GeneticOperators geneticOperators,
GeneticAlgorithmSettings settings, ICollection<Individual> startingPopulation = null,
uint currentGeneration = 0, Guid? id = null)
: base(graph, problem, id)
{
if (geneticOperators == null)
throw new ArgumentNullException(nameof(geneticOperators));
if (!geneticOperators.IsValid())
throw new ArgumentException("Genetic operators are not valid", nameof(geneticOperators));
if (settings == null)
throw new ArgumentNullException(nameof(settings));
GeneticOperators = geneticOperators;
Settings = settings;
if (startingPopulation == null || startingPopulation.Count == 0 || !startingPopulation.All(IsIndividualValid))
GenerateInitialPopulation();
else
{
CurrentPopulation = new SortedSet<Individual>();
foreach (var element in startingPopulation)
CurrentPopulation.Add(element);
}
CurrentGeneration = currentGeneration;
}
public void RealiseEvolution()
{
// Initialisation
Initialisation initialisation = new Initialisation(initialPopulationCount, scale);
List <Individual> population = initialisation.GenerateInitialPopulation();
// Validation
foreach (Individual representation in population)
{
if (StaticOperations.ValidateIndividual(representation) == false)
{
throw new NotSupportedException();
}
}
// Evaluation
Evaluation evaluation = new Evaluation(scale);
foreach (Individual representation in population)
{
evaluation.EvaluateIndividual(representation);
}
// Evolution cycles
for (int i = 0; i < evolutionCycles; i++)
{
Console.Write("Epoch #" + i + " ");
// Selection
Selection selection = new Selection(initialPopulationCount, population);
List <Individual> parents = selection.SelectParents(2);
// Genetic operators
List <Individual> descendants = new List <Individual>();
GeneticOperators geneticOperators = new GeneticOperators(scale);
for (int j = 0; j < parents.Count; j = j + 2)
{
descendants.AddRange(geneticOperators.GenerateDescendants(parents[j], parents[j + 1]));
}
// Evaluation
foreach (Individual representation in descendants)
{
evaluation.EvaluateIndividual(representation);
}
// Validation
foreach (Individual representation in population)
{
if (StaticOperations.ValidateIndividual(representation) == false)
{
throw new NotSupportedException();
}
}
// Replacement
Replacement replacement = new Replacement(population, descendants, initialPopulationCount);
population = replacement.NextGeneration();
List <Individual> orderedPopulation = population.OrderBy(item => item.Fitness).ToList();
Console.WriteLine("Best individual has fitness: " + orderedPopulation[0].Fitness);
}
SaveBestIndividualIntoFile(population[0]);
}
public void RealiseEvolution()
{
// Initialise population - validated
Initialisation initialisation = new Initialisation(initialPopulationCount);
List <Individual> population = initialisation.GenerateInitialPopulation();
foreach (Individual individual in population)
{
if (StaticOperations.ValidateIndividual(individual) == false)
{
throw new NotSupportedException();
}
}
// Evaluate synthethic fitness of population
Evaluation evaluation = new Evaluation(firstCriteria, secondCriteria, thirdCriteria);
foreach (Individual individual in population)
{
// Only criteria with true value will be considered
evaluation.EvaluateIndividual(individual);
}
//Evolution cycles
for (int i = 0; i < evolutionCycles; i++)
{
Console.Write("Epoch #" + i);
// Selection - q-tournament
Selection selection = new Selection(population, initialPopulationCount);
List <Individual> parents = selection.SelectParents(2);
// 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
foreach (Individual individual in descendants)
{
// Only criteria with true value will be considered
evaluation.EvaluateIndividual(individual);
}
// Replacement
Replacement replacement = new Replacement(population, descendants, initialPopulationCount);
population = replacement.NextGeneration();
// Save best individual
if (firstCriteria)
{
List <Individual> paretSetSurvivors = population.Where(ind => ind.Fitness == 1 && ind.FitnessVector[0] == 1).ToList();
if (paretSetSurvivors.Count == 0)
{
bestIndividuals.Add(population[0]);
}
else
{
paretSetSurvivors.Shuffle();
bestIndividuals.Add(paretSetSurvivors[0]);
}
Console.WriteLine(" Paret set count: " + EvaluateParetSet(population));
}
else
{
List <Individual> paretSetSurvivors = population.Where(ind => ind.Fitness == 1).ToList();
paretSetSurvivors.Shuffle();
bestIndividuals.Add(paretSetSurvivors[0]);
Console.WriteLine(" Paret set count: " + EvaluateParetSet(population));
}
}
SaveBestIndividualsInFile();
}
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();
}
