static void Main()
{
IGAconfiguration configuration = new GAConfiguration(100, 0.9, null, 0.6, 0.3);
IList<IIndividual<Wrapper<MyValuableClass>>> individualsList = new List<IIndividual<Wrapper<MyValuableClass>>>();
//Individuals initializing
for (int i = 0; i < 1000; i++)
{
//Wrap source LOs to pass to GA
List<Wrapper<MyValuableClass>> wrappedList = new List<Wrapper<MyValuableClass>>();
for (int j = 0; j < 5; j++)
{
wrappedList.Add(new Wrapper<MyValuableClass>(new MyValuableClass(GAUtils.GetRandom(0, 100))));
}
individualsList.Add(new Individual<Wrapper<MyValuableClass>>(wrappedList));
}
Console.WriteLine("Population creating");
var population = new Population<Wrapper<MyValuableClass>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
IGeneticAlgorithm<Wrapper<MyValuableClass>> ga = new GA<Wrapper<MyValuableClass>>(population);
ObjectId id = new ObjectId();
Console.WriteLine("GA launching");
BackgroundWorker bgBackgroundWorker = new BackgroundWorker();
ga.Run(StopCondition, configuration, id, bgBackgroundWorker, WriteToCOnsole);
foreach (var val in ga.Statistics.AverageFitnessFunctionValues)
{
Console.WriteLine(val);
}
string fileName = "data.txt";
using (StreamWriter outfile = new StreamWriter(fileName))
{
foreach (var averageFitnessFunctionValue in ga.Statistics.AverageFitnessFunctionValues)
{
outfile.WriteLine(averageFitnessFunctionValue);
}
}
//ga.Run(statistics => statistics.CurrentIteration > configuration.IterationsNumber, configuration, id, bgBackgroundWorker, WriteToCOnsole);
}
private void bw_DoWork(object sender, DoWorkEventArgs e)
{
BackgroundWorker worker = sender as BackgroundWorker;
GaTask task = (GaTask)e.Argument;
try
{
task.ProgressPercent = 0;
task.State = TaskState.Launched;
task.LaunchTime = DateTime.Now;
IList<IIndividual<Wrapper<LearningObject>>> individualsList = new List<IIndividual<Wrapper<LearningObject>>>();
//Individuals initializing
for (int i = 0; i < task.GaConfigs.PopulationSize; i++)
{
//Wrap source LOs to pass to GA
//List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>(); = task.SourceLearningObjects.Select(sourceLearningObject => new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject)).ToList();
List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>();
foreach (var sourceLearningObject in task.SourceLearningObjects)
{
wrappedListOfLo.Add(new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject));
}
wrappedListOfLo = GAUtils.Shuffle(wrappedListOfLo).ToList();
individualsList.Add(new Individual<Wrapper<LearningObject>>(wrappedListOfLo));
}
var population = new Population<Wrapper<LearningObject>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
IGeneticAlgorithm<Wrapper<LearningObject>> ga = new GA<Wrapper<LearningObject>>(population);
IGAconfiguration configuration = new GAConfiguration(task.GaConfigs.GAIterationsNumber,
task.GaConfigs.MutationProbability, task.GaConfigs.MutationPeriod, task.GaConfigs.CrossoverProbability,
task.GaConfigs.ElitismPercentage);
ga.Run(StopCondition, configuration, task.Id, worker);
if (worker != null && worker.CancellationPending)
{
task.State = TaskState.Cancelled;
e.Result = task;
}
else
{
task.State = TaskState.Finished;
}
if (task.State == TaskState.Finished)
{
if (worker != null) worker.ReportProgress(100, task.Id);
}
//Put result data to task
task.AverageFFvalues = ga.Statistics.AverageFitnessFunctionValues;
task.BestFfValues = ga.Statistics.BestFitnessFunctionValues;
task.WorstFfValues = ga.Statistics.WorstFitnessFunctionValues;
List<LearningObject> resultList = new List<LearningObject>();
foreach (var wrapper in ga.BestIndividual.Chromosome.Genes)
{
if (wrapper.Used)
{
resultList.Add(wrapper.Value);
}
}
task.ResultLearningObjects = resultList;
e.Result = task;
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
task.State = TaskState.Error;
task.ErrorMessage = ex.Message;
e.Result = task;
}
}
static void Main(string[] args)
{
GA Ga;
TravellingSalesmanObjective Objective;
Galib.Mutation.IntegerSwapMutator Mutator;
IntegerChromosomeSpecifier ChromosomeSpecifier;
//
// Create the objective objective.
//
int numberOfCities = 10;
Objective = new TravellingSalesmanObjective(numberOfCities);
//
// Create the mutator
//
Mutator = new Galib.Mutation.IntegerSwapMutator();
//
// Create the chromosome specifier.
//
ChromosomeSpecifier = new IntegerChromosomeSpecifier();
ChromosomeSpecifier.Length = numberOfCities;
ChromosomeSpecifier.MinValue = 0;
ChromosomeSpecifier.MaxValue = numberOfCities;
ChromosomeSpecifier.PositionDependent = true;
//
// Create the recombinator
//
Galib.Recombination.PartiallyMatchedRecombinator Recombinator;
Recombinator = new Galib.Recombination.PartiallyMatchedRecombinator();
Ga = new GA();
Ga.PopulationSize = 100;
Ga.Mutator = Mutator;
Ga.ChromosomeSpecifier = ChromosomeSpecifier;
Ga.Objective = Objective;
Ga.InvertedObjective = true;
Ga.Recombinator = Recombinator;
Ga.MaxGenerations = 500;
Ga.Run();
Console.WriteLine("Best Individual:");
Console.WriteLine( Ga.Population.BestChromosome );
Console.WriteLine("Finished. Hit return.");
Console.ReadLine();
}