/// <summary>
/// Ends the current generation.
/// </summary>
/// <returns><c>true</c>, if current generation was ended, <c>false</c> otherwise.</returns>
private bool EndCurrentGeneration()
{
EvaluateFitness();
Population.EndCurrentGeneration();
var handler = GenerationRan;
if (handler != null)
{
handler(this, EventArgs.Empty);
}
if (Termination.HasReached(this))
{
State = GeneticAlgorithmState.TerminationReached;
handler = TerminationReached;
if (handler != null)
{
handler(this, EventArgs.Empty);
}
return(true);
}
if (m_stopRequested)
{
TaskExecutor.Stop();
State = GeneticAlgorithmState.Stopped;
}
return(false);
}
/// <summary>
/// Resumes the last evolution of the genetic algorithm.
/// <remarks>
/// If genetic algorithm was not explicit Stop (calling Stop method), you will need provide a new extended Termination.
/// </remarks>
/// </summary>
public void Resume()
{
try
{
lock (m_lock)
{
m_stopRequested = false;
}
if (Population.GenerationsNumber == 0)
{
throw new InvalidOperationException("Attempt to resume a genetic algorithm which was not yet started.");
}
if (Population.GenerationsNumber > 1)
{
if (Termination.HasReached(this))
{
throw new InvalidOperationException("Attempt to resume a genetic algorithm with a termination ({0}) already reached. Please, specify a new termination or extend the current one.".With(Termination));
}
State = GeneticAlgorithmState.Resumed;
}
if (EndCurrentGeneration())
{
return;
}
bool terminationConditionReached = false;
DateTime startDateTime;
do
{
if (m_stopRequested)
{
break;
}
startDateTime = DateTime.Now;
terminationConditionReached = EvolveOneGeneration();
TimeEvolving += DateTime.Now - startDateTime;
}while (!terminationConditionReached);
}
catch
{
State = GeneticAlgorithmState.Stopped;
throw;
}
}
public void EvolveNextGeneration()
{
try
{
lock (m_lock)
{
m_stopRequested = false;
}
if (Population.GenerationsNumber == 0)
{
throw new InvalidOperationException("Attempt to resume a genetic algorithm which was not yet started.");
}
if (Population.GenerationsNumber > 1)
{
if (Termination.HasReached(this))
{
throw new InvalidOperationException("Attempt to resume a genetic algorithm with a termination ({0}) already reached. Please, specify a new termination or extend the current one.".With(Termination));
}
State = GeneticAlgorithmState.Resumed;
}
if (EndCurrentGeneration())
{
return;
}
bool terminationConditionReached = false;
DateTime startDateTime;
startDateTime = DateTime.Now;
var parents = SelectParents();
var offspring = Cross(parents);
Mutate(offspring);
var newGenerationChromosomes = Reinsert(offspring, parents);
Population.CreateNewGeneration(newGenerationChromosomes);
TimeEvolving += DateTime.Now - startDateTime;
}
catch
{
State = GeneticAlgorithmState.Stopped;
throw;
}
}
/// <summary>Assigns fitness function value to offsprings and registers new generation in a population.</summary>
/// <param name="parents">Parents obtained form the ProduceOffsprings node.</param>
/// <param name="offsprings">Offsprings obtained form the ProduceOffsprings node.</param>
/// <param name="offspringsFitness">A list of fitness function results for offsprings.</param>
/// <returns name="algorithm">A genetic algorithm instance supplemented with a new generation.</returns>
public void ProduceNewGeneration(List <object> parents, List <object> offsprings, List <double> offspringsFitness)
{
if (State == GeneticAlgorithmState.TerminationReached)
{
return;
}
IList <IChromosome> parentsChromosomes = new List <IChromosome>();
foreach (object parent in parents)
{
parentsChromosomes.Add((IChromosome)parent);
}
IList <IChromosome> offspringsChromosomes = new List <IChromosome>();
foreach (object offspring in offsprings)
{
offspringsChromosomes.Add((IChromosome)offspring);
offspringsChromosomes.Last().Fitness = offspringsFitness[offsprings.IndexOf(offspring)];
}
Population.MinSize = Population.MaxSize;
var newGenerationChromosomes = Reinsert(offspringsChromosomes, parentsChromosomes);
while (newGenerationChromosomes.Count < Population.CurrentGeneration.Chromosomes.Count())
{
newGenerationChromosomes = Reinsert(newGenerationChromosomes, parentsChromosomes);
}
Population.CreateNewGeneration(newGenerationChromosomes);
Population.EndCurrentGeneration();
if (Termination.HasReached(this) || (Population.GenerationsNumber >= maxNumberOfIterations))
{
State = GeneticAlgorithmState.TerminationReached;
TimeEvolving = Timer.Elapsed;
Timer.Stop();
}
}
/// <summary>
/// Ends the current generation.
/// </summary>
/// <returns><c>true</c>, if current generation was ended, <c>false</c> otherwise.</returns>
private bool EndCurrentGeneration()
{
EvaluateFitness();
Population.EndCurrentGeneration();
if (GenerationRan != null)
{
GenerationRan(this, EventArgs.Empty);
}
if (Termination.HasReached(this))
{
State = GeneticAlgorithmState.TerminationReached;
if (TerminationReached != null)
{
TerminationReached(this, EventArgs.Empty);
}
//Console.ForegroundColor = ConsoleColor.DarkGreen;
//Console.WriteLine("Pick List Genetic Algorithm termination criteria are met.");
//Console.ResetColor();
return(true);
}
if (m_stopRequested)
{
TaskExecutor.Stop();
State = GeneticAlgorithmState.Stopped;
}
//Console.ForegroundColor = ConsoleColor.Cyan;
//Console.WriteLine("Pick List Genetic Algorithm will continue to evolve...");
//Console.ResetColor();
//Console.WriteLine();
return(false);
}
/// <summary>
/// Ends the current generation.
/// </summary>
/// <returns><c>true</c>, if current generation was ended, <c>false</c> otherwise.</returns>
private bool EndCurrentGeneration()
{
if (_debugging)
{
Console.WriteLine("Starting fitness evaluation");
timer.Reset();
timer.Start();
}
// Insert multithreading here?
try
{
foreach (var spec in Species)
{
TaskExecutorFitMaster.Add(() => {
EvaluateFitness(spec);
});
}
if (!TaskExecutorFitMaster.Start())
{
throw new TimeoutException("The fitness evaluation rech the {0} timeout.".With(TaskExecutorFitMaster.Timeout));
}
}
finally
{
TaskExecutorFitMaster.Stop();
TaskExecutorFitMaster.Clear();
}
if (_debugging)
{
Console.WriteLine("Evolved a generation. About to calculate fitness and end it. Time at {0}", timer.Elapsed.ToString());
timer.Stop();
}
if (_debugging)
{
Console.WriteLine("Calculating Fitness done. Timer at {0}", timer.Elapsed);
timer.Stop();
timer.Reset();
Console.WriteLine("Starting reinsertion");
timer.Start();
}
foreach (var spec in Species)
{
spec.EndCurrentGeneration();
}
if (_debugging)
{
Console.WriteLine("Reinsertion done. Timer at {0}", timer.Elapsed);
timer.Stop();
}
GenerationsNumber++;
//update uber best set
if (UberBestSet == null || UberBestSet.Count == 0)
{
UberBestSet = new List <IChromosome> ();
foreach (IChromosome c in BestChromosomeSet)
{
UberBestSet.Add(c.Clone());
}
}
else
{
for (int i = 0; i < BestChromosomeSet.Count; i++)
{
if (UberBestSet [i].Fitness < BestChromosomeSet [i].Fitness)
{
UberBestSet [i] = BestChromosomeSet [i].Clone();
}
}
}
if (GenerationRan != null)
{
GenerationRan(this, EventArgs.Empty);
}
if (Termination.HasReached(this))
{
State = GeneticAlgorithmState.TerminationReached;
if (TerminationReached != null)
{
TerminationReached(this, EventArgs.Empty);
}
return(true);
}
if (m_stopRequested)
{
TaskExecutorGen.Stop();
TaskExecutorFit.Stop();
State = GeneticAlgorithmState.Stopped;
}
return(false);
}