public static void Main(string[] args)
{
Events.Quit += new EventHandler<QuitEventArgs>(ApplicationQuitEventHandler);
Surface plate = Video.SetVideoMode(220, 220, 32, false, false, false, true);
Surface pic = new Surface("nyanbig.png");
pic.Update();
Population fellows = new Population(15, 100);
List<float> fitnesses;
while (fellows.members[0].fitness < 700f)
{
fitnesses = new List<float>();
foreach (Genome g in fellows.members)
{
g.Draw(plate);
Console.Read();
float f = g.GetFitness(plate, pic);
fitnesses.Add(f);
g.fitness = f;
}
fellows.members.Sort();
fellows.members[0].Draw(plate);
Console.WriteLine(fellows.members[0].fitness);
int x = 0;
}
}
static void Main(string[] args)
{
Population p = new Population();
p.WriteNextGeneration();
int count = 0;
byte res = 0;
EquationGenome resGenome;
while (p.Converged() == false)
{
p.NextGeneration();
if (count % 50 == 0) {
p.WriteNextGeneration();
resGenome = (EquationGenome) p.GetNextGenerationBest();
res = resGenome.PerformCalculation(0, 1, 1, 1);
Console.WriteLine("<Loop> Result => {0}", res.ToString());
}
count++;
if (count > 40000) {
Console.WriteLine("---------------");
p.WriteNextGenerationBest();
Console.WriteLine("---------------");
break;
} // End of if
} // End of the while
Console.WriteLine("Done");
resGenome = (EquationGenome) p.GetNextGenerationBest();
res = resGenome.PerformCalculation(0, 1, 1, 1);
Console.WriteLine("Result => {0}", res.ToString());
}
static void Main(string[] args)
{
//This code runs a standard search for a randomly assigned alphanumeric string with 80% accuracy
//(i.e. 2 non matching "genes" allowed in solution organism)
DateTime start = DateTime.Now;
Population pop1 = new Population(100);
DateTime now = DateTime.Now;
Console.WriteLine("Time taken was: " + (now-start).TotalSeconds + " seconds");
Console.Out.WriteLine("Press Enter to Continue...");
Console.In.ReadLine();
//this code tests the genetic algorithm, organisms, and chromosomes
//GeneticAlgorithmTester tester = new GeneticAlgorithmTester();
}
public void AddPopulationThread(string name)
{
TestPopulation = new Population(name);
Populations.Add(TestPopulation);
ThreadPool.QueueUserWorkItem(new WaitCallback(this.RunPopulation), (object)TestPopulation);
}
static void Main(string[] args)
{
TestData.GetData(out var servers, out var services);
var chromosome = new MyChromosome(servers, services);
var populationSize = 9;
var population = new Population(populationSize, populationSize, chromosome);
var fitness = new FuncFitness((c) =>
{
var mc = (MyChromosome)c;
var genes = mc.GetGenes();
var servers = genes.Select(x => (Server)x.Value).ToList();
var freeServersTerm = servers.Count(x => x.IsFree) * 2.5;
var negativeHddFreeTerm = servers.Where(x => x.HddFree < 0).Select(x => x.HddFree * 3).Sum();
var positiveHddFreeTerm = servers.Where(x => x.HddFree > 0).Select(x => x.HddFree * 0.2).Sum();
var negativeRamFreeTerm = servers.Where(x => x.RamFree < 0).Select(x => x.RamFree * 3).Sum();
var positiveRamFreeTerm = servers.Where(x => x.RamFree > 0).Select(x => x.RamFree * 0.2).Sum();
var negativeCpuFreeTerm = servers.Where(x => x.CpuFree < 0).Select(x => x.CpuFree * 3).Sum();
var positiveCpuFreeTerm = servers.Where(x => x.CpuFree > 0).Select(x => x.CpuFree * 0.2).Sum();
var fitness = freeServersTerm + negativeHddFreeTerm + positiveHddFreeTerm + negativeRamFreeTerm +
positiveRamFreeTerm + negativeCpuFreeTerm + positiveCpuFreeTerm;
//по каждому серверу нужно вычислить коэффициент заполненности
var serversFill = new List <double>();
foreach (var server in servers)
{
var serverFill = ((server.HddFull - server.HddFree) / server.HddFull) * 0.2 +
((server.RamFull - server.RamFree) / server.RamFull) * 0.4 +
((server.CpuFull - server.CpuFree) / server.CpuFull) * 0.4;
serversFill.Add(serverFill);
}
serversFill = serversFill.OrderByDescending(x => x).ToList();
//отладочная строка
//var serversFill = new List<double> { 0.9d, 0.9d, 0.6d, 0.2d,0 };
//среднее значение заполненности серверов
var averageFill = serversFill.Sum() / servers.Count();
//рассчитаем дисперсию у serversFill
var dispersion = serversFill.Select(eachFill => (eachFill - averageFill) * (eachFill - averageFill)).Sum() / servers.Count;
return((dispersion * 3) + fitness);
});
var selection = new EliteSelection();
//сделал свой кроссовер, похожий на UniformCrossover
var crossover = new MyCrossover();
//строка для отладки кроссовера
//crossover.Cross(new List<IChromosome> {chromosome.CreateNew(), chromosome.CreateNew()});
var mutation = new MyMutation();
//mutation.Mutate(chromosome, Single.Epsilon);
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation)
{
Termination = termination,
MutationProbability = 0.3f
};
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var ga = (GeneticSharp.Domain.GeneticAlgorithm)sender;
Console.Write("Номер итерации: " + ga.GenerationsNumber + " " + "Время работы: " + ga.TimeEvolving);
Console.WriteLine();
var bestChromosome = ga.BestChromosome as MyChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.GetGenes()
.Select(x => (Server)x.Value).ToList();
Console.Write("Фитнес: " + bestFitness);
Console.WriteLine();
foreach (var server in phenotype)
{
Console.Write("Сервер: " + server.Name +
", HddFull: " + server.HddFull +
", RamFull: " + server.RamFull +
", HddFree: " + server.HddFree +
", RamFree: " + server.RamFree +
", CpuFree: " + server.CpuFree +
", сервисы: ");
Console.Write(" Заполненность Hdd: " + Math.Round(100 - (server.HddFree / server.HddFull * 100)) + "%");
Console.Write(" Заполненность Ram: " + Math.Round(100 - (server.RamFree / server.RamFull * 100)) + "%");
Console.Write(" Заполненность Cpu: " + Math.Round(100 - server.CpuFree) + "%");
foreach (var service in server.Services)
{
Console.Write(service.Name + ", ");
}
Console.WriteLine();
Console.WriteLine();
//получить хромосомы на этой итерации и смотреть, равны ли они.
//Хромосомы равны, если равны их фитнесы.
//Если равны, алгоритм преждевременно сходится (вырождается популяция)
var chromosomesFitnessCollection = ga.Population.CurrentGeneration.Chromosomes;
}
}
};
ga.Start();
}
protected abstract IList <ChromoSome> AdjustInternal(Population population, IList <ChromoSome> children, IList <ChromoSome> parents);
public InternalSearchResult(Population population, TimeSpan searchTime, bool isCompleted)
{
SearchTime = searchTime;
IsCompleted = isCompleted;
Population = population;
}