private ScheduleChromosome RunOnce(LineSeries lineSeries = null)
{
var adamChromosome = new ScheduleChromosome(jobShop);
var population = new Population(Global.Config.MinPopulationSize, Global.Config.MaxPopulationSize, adamChromosome);
var fitness = new ScheduleFitness();
var selection = new NonDeterministicTournamentSelection(Global.Config.TournamentSelectionProbability);
var crossover = new SchedulesCrossover(new CycleCrossover());
var mutation = new ScheduleMutation(Global.Config.MutationPerGeneProbability, new ReverseSequenceMutation());
var geneticAlgorithm =
new GeneticSharp.Domain.GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(Global.Config.GenerationsCount),
MutationProbability = Global.Config.MutationProbability,
CrossoverProbability = Global.Config.CrossoverProbability,
OperatorsStrategy = new JobShopOperatorStrategy(),
Reinsertion = new JobShopReinsertion(
new EliteSelection(),
new Elitism(Global.Config.ElitismPercent),
fitness
)
};
var stopWatch = new Stopwatch();
if (adaptive)
{
geneticAlgorithm.GenerationRan += (sender, e) => AdaptMutationProbability(geneticAlgorithm);;
}
geneticAlgorithm.GenerationRan += (sender, e) =>
{
Print(geneticAlgorithm.Population, stopWatch.Elapsed);
};
// plot the model
if (PlotModel != null)
{
geneticAlgorithm.GenerationRan += (sender, e) =>
{
lineSeries.Points.Add(new DataPoint(geneticAlgorithm.Population.GenerationsNumber,
((ScheduleChromosome)geneticAlgorithm.Population.BestChromosome).ScheduleLength.Value));
};
}
geneticAlgorithm.GenerationRan += (o, e) => GenerationRan?.Invoke(geneticAlgorithm.Population.CurrentGeneration);
if (Global.Config.ThreadsCount > 1)
{
geneticAlgorithm.TaskExecutor = new ParallelTaskExecutor()
{
MinThreads = 1,
MaxThreads = Global.Config.ThreadsCount
};
}
stopWatch.Start();
geneticAlgorithm.Start();
stopWatch.Stop();
return((ScheduleChromosome)geneticAlgorithm.BestChromosome);
}
//Одне скомбіноване рішення
//Недоречний варіант!!!
public void Start()
{
//Сам генетический алгоритм
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
_population,
_fitness,
_selection,
_crossover,
_mutation);
ga.Termination = _termination;
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as BinaryChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.GetGenes();
Console.WriteLine("Поколiння №{0,2}. Кращий результат = {1}", ga.GenerationsNumber, bestFitness);
Console.Write("Вид хромосоми: ");
foreach (GeneticSharp.Domain.Chromosomes.Gene g in phenotype)
{
Console.Write(g.Value.ToString() + "");
}
Console.WriteLine();
}
};
ga.Start();
}
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population(20, 20, chromosome);
var ga = new GeneticSharp.Domain.GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(100)
};
Console.WriteLine("GA running...");
ga.Start();
if (ga.BestChromosome.Fitness != null)
{
double fitnessValue = ga.BestChromosome.Fitness.Value;
string solution = ga.BestChromosome.ToUserFriendly();
Console.WriteLine($"Best solution {solution} found has {fitnessValue} fitness for {ga.GenerationsNumber} generations.");
}
}
//Запустити генетичний алгоритм public void Start()//GeneticSharp.Domain.GeneticAlgorithm ga) => _ga.Start();
