public static GeneticSharp.Domain.GeneticAlgorithm DefaultGeneticAlgorithm(Func <double[], double> func, Tuple <double, double>[] minmax)
{
var population = new Population(20, 40, chromosome.CreateIntChromosone(minmax));
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
return(func(values));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
var termination = new FitnessStagnationTermination(100);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
return(ga);
}
public AnalysisResults Optimize(PixelStructure structure)
{
var chromosome = new StructuralChromosome(structure.Pixels.Count);
var population = new Population(5, 10, chromosome);
var fitness = new StructuralFitness(structure);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(); //FlipBitMutation();
var termination = new FitnessStagnationTermination(10);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.GenerationRan += GenerationRan;
ga.Start();
var structRes = ga.BestChromosome as StructuralChromosome;
return(structRes.Results);
}
public void InitiGA(GeneticAlgoritmConfig config)
{
Config = config;
//IChromosome adamChromosome = new FloatingPointChromosome(config.MinChromosomeValue, config.MaxChromosomeValue, config.ChromosomeSize, config.ChromosomeFractionDigits);
float maxWidth = 1000000;
float maxHeight = 1000000;
IChromosome adamChromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 64, 64, 64, 64 },
new int[] { 2, 2, 2, 2 });
Population population = new Population(config.MinPopulationSize, config.MinPopulationSize, adamChromosome);
IFitness fitness = new SimpleFitness();
ISelection selection = new EliteSelection();
ICrossover crossover = new UniformCrossover(0.1f);
IMutation mutation = new FlipBitMutation();
ITermination termination = new FitnessStagnationTermination(config.StagnationGenerationCount);
population.GenerationStrategy = new PerformanceGenerationStrategy();
geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination
};
geneticAlgorithm.CrossoverProbability = config.CrossoverProbability;
geneticAlgorithm.MutationProbability = config.MutationProbability;
geneticAlgorithm.GenerationRan += OnNewGeneration;
geneticAlgorithm.TerminationReached += OnTermination;
geneticAlgorithm.Start();
}
public void HasReached_StagnantAndReachGenerationNumber_True()
{
var target = new FitnessStagnationTermination(3);
var ga = Substitute.For <IGeneticAlgorithm>();
ga.BestChromosome.Returns(new ChromosomeStub()
{
Fitness = 0.2
},
new ChromosomeStub()
{
Fitness = 0.2
},
new ChromosomeStub()
{
Fitness = 0.3
},
new ChromosomeStub()
{
Fitness = 0.3
},
new ChromosomeStub()
{
Fitness = 0.3
});
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
Assert.IsTrue(target.HasReached(ga));
}
public override ITermination CreateTermination()
{
var terminations = new TimeEvolvingTermination(TimeSpan.FromSeconds(this.settings.MaxOptimizationTimeSeconds));
var fitnessStagnationTermination = new FitnessStagnationTermination(this.settings.MaxOptimizationStagnatingGenerations);
return(new OrTermination(terminations, fitnessStagnationTermination));
}
public void HasReached_StagnantButNotReachTheGenerationsNumber_False()
{
var target = new FitnessStagnationTermination(4);
var repository = new MockRepository();
var ga = repository.StrictMock <IGeneticAlgorithm>();
using (repository.Ordered())
{
ga.Expect(g => g.BestChromosome).Return(new ChromosomeStub()
{
Fitness = 0.1
});
ga.Expect(g => g.BestChromosome).Return(new ChromosomeStub()
{
Fitness = 0.1
});
ga.Expect(g => g.BestChromosome).Return(new ChromosomeStub()
{
Fitness = 0.1
});
}
repository.ReplayAll();
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
}
public ITermination FitnessStagnation()
{
var target = new FitnessStagnationTermination(2);
var ga = Substitute.For <IGeneticAlgorithm>();
ga.BestChromosome.Returns(new TspChromosome(10)
{
Fitness = 1
});
target.HasReached(ga);
target.HasReached(ga);
return(target);
}
public void Start_FloatingPoingChromosome_Evolved()
{
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { 1000, 1000, 1000, 1000 },
new int[] { 10, 10, 10, 10 },
new int[] { 0, 0, 0, 0 });
var population = new Population(25, 25, chromosome);
var fitness = new FuncFitness((c) =>
{
var f = c as FloatingPointChromosome;
var values = f.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
// Euclidean distance: https://en.wikipedia.org/wiki/Euclidean_distance
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.Start();
var bc = ga.BestChromosome as FloatingPointChromosome;
var points = bc.ToFloatingPoints();
Assert.AreEqual(4, points.Length);
Assert.AreEqual(1414.2135623730951, bc.Fitness);
Assert.GreaterOrEqual(ga.GenerationsNumber, 100);
}
public static GeneticAlgorithm GetGeneticAlgorithm()
{
var chromosome = new ConfigurationChromosome();
var population = new Population(50, 100, chromosome);
var fitness = new CorrectPercentageFitness();
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation)
{
Termination = termination
};
return(ga);
}
public void HasReached_StagnantButNotReachTheGenerationsNumber_False()
{
var target = new FitnessStagnationTermination(4);
var ga = Substitute.For <IGeneticAlgorithm>();
ga.BestChromosome.Returns(
new ChromosomeStub()
{
Fitness = 0.1
},
new ChromosomeStub()
{
Fitness = 0.1
},
new ChromosomeStub()
{
Fitness = 0.1
});
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
Assert.IsFalse(target.HasReached(ga));
}
public CPSetup(int populationSize, string filename = "")
{
if (String.IsNullOrEmpty(filename))
{
Console.WriteLine("Podaj nazwę pliku z grafem: ");
var builder = new StringBuilder();
builder.Append("Data/");
builder.Append(Console.ReadLine());
filename = builder.ToString();
}
Graph = new Graph(@filename);
int[] startValues = Graph.OddVertices.ToArray();
Fitness = new CPFitness();
Chromosome = new CPChromosome(startValues.Length, startValues, populationSize);
Population = new Population(populationSize, populationSize, Chromosome);
Selection = new EliteSelection();
Crossover = new CPCrossover();
Mutation = new CPMutation();
Termination = new FitnessStagnationTermination(50);
GA = new GeneticAlgorithm(Population, Fitness, Selection, Crossover, Mutation);
GA.Termination = Termination;
}
public static FloatingPointChromosome EvolveGeneticAlgorithm(FloatingPointChromosome chromosome, IOthelloAgent agent, string chromosomeLabel = "")
{
IPopulation population = new TplPopulation(30, 60, chromosome);
IFitness fitness = new EvaluationFitness(agent);
ISelection selection = new RouletteWheelSelection(); //Guess
ICrossover crossover = new UniformCrossover(); //Guess
IMutation mutation = new UniformMutation(); //Guess
ITermination stagnation = new FitnessStagnationTermination(500);
ITermination threshold = new FitnessThresholdTermination(.9);
ITaskExecutor taskExecutor = new ParallelTaskExecutor()
{
MaxThreads = Environment.ProcessorCount,
MinThreads = Environment.ProcessorCount / 2
};
GeneticAlgorithm algorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
TaskExecutor = new TplTaskExecutor(),
MutationProbability = .2f
};
algorithm.TaskExecutor = taskExecutor;
algorithm.Termination = stagnation;
algorithm.Start();
SaveChromosome((FloatingPointChromosome)algorithm.BestChromosome, chromosomeLabel);
Debug.WriteLine("finished Training, with {0} time spent on evolving", algorithm.TimeEvolving);
Debug.WriteLine("fitness of this generation vs the last : {0}", algorithm.Fitness);
return((FloatingPointChromosome)algorithm.BestChromosome);
}
static void Main(string[] args)
{
string cadena; //cadena leida por el usuario
int tam_cadena; //tamaño de la cadena introducida
string cadenana = ""; //cadena resultante generada por el algoritmo
Console.WriteLine("Introduzca la cadena");
cadena = Console.ReadLine();
cadena = cadena.Trim(); //se eliminan espacios de la cadena introducida
cadena = cadena.ToLower(); //se pasa a minusculas todas las letras
tam_cadena = cadena.Length; //se obtiene le tamaño de la caden
double[] min = new double[tam_cadena]; //se generan
double[] max = new double[tam_cadena]; //rangos minimos y maximos para que trabaje el AG
int[] arr = new int[tam_cadena]; //se generan los arreglos
int[] arr2 = new int[tam_cadena]; // de numero de digits , bits
for (int i = 0; i < tam_cadena; i++) //se debe de llenar una posicion del arreglo por cada gen(cada caracter de la cadena )
{
min[i] = 97; //minimo -> a
max[i] = 122; //maximo -> z
arr[i] = 64;
arr2[i] = 3;
}
var cromosoma = new FloatingPointChromosome(min, max, arr, arr2); //se instancia la nueva cromosoma
var aptitud = new FuncFitness((c) => { //se define la funcion de aptitud
var fc = c as FloatingPointChromosome;
var x = fc.ToFloatingPoints();
string cad_obt = "";
double porcentaje;
for (int i = 0; i < tam_cadena; i++) //para cada gen
{
cad_obt += (char)Convert.ToInt32(x[i]); //se debe de concatenar a la cadena obtenida, convirtiendo de float a int y despues
}
//casteando a caracter
double dist = Math.Exp(-LevenshteinDistance(cadena, cad_obt, out porcentaje)); //se realiza el calculo
return(1f / 1f + dist); //de la aptitud
});
var poblacion = new Population(100, 1000, cromosoma); //se crea la poblacon con minimo 100 individuos y maximo 1000
var cruza = new UniformCrossover(0.3f); //se define como se realizara la cruza
var mutacion = new FlipBitMutation(); //como se realizara la mutacion
var seleccion = new RouletteWheelSelection(); //se elige el metodo de seleccion a utilizar
var terminacion = new FitnessStagnationTermination(1000); //se determina el parametro de termino para el algoritmo genetico
//var ga = new GeneticSharp
var ga = new GeneticAlgorithm(poblacion, aptitud, seleccion, cruza, mutacion); //se crea el algoritmo genetico
ga.Termination = terminacion; //se asigna la condicion de termino a el AG
var latestFitness = 0.0; //variable para guardar fitness anterior
ga.GenerationRan += (sender, e) => //funcion para estar verificando como es que evoluciona el AG
{
var bestchromosome = ga.BestChromosome as FloatingPointChromosome; //se obtiene la cromosoma
var bestFitness = bestchromosome.Fitness.Value; //se obtiene el fitness de la cromosoma
if (bestFitness != latestFitness) //se comprueba que el fitness actual sea distinto al anterior
{
latestFitness = bestFitness; //el anterior fitness se vuelve el mejor ahora, para asi comprobar que le fitness mejora
// var phenotype = bestchromosome.ToFloatingPoints();//se obtienen los valores que reprsenta la cromosoma
Console.WriteLine("Generation {0}", ga.GenerationsNumber); //se imprime el numero de generacion
// Console.WriteLine("X = {0}", bestchromosome.ToFloatingPoint());
var x = bestchromosome.ToFloatingPoints(); //se obtienen los valores que reprsenta la cromosoma
cadenana = "";
for (int i = 0; i < tam_cadena; i++) //se obtiene el valor
{
cadenana += (char)Convert.ToInt32(x[i]); // que representa el fenotipo en terminos de caracteres para
}
//formar la cadena resultante
// Console.WriteLine("F(x) = {0}", (char)((int)bestFitness));
Console.WriteLine("Palabra Obtenida " + cadenana);//imprime la mejor aproximacion hasta ahora
}
};
ga.Start(); //se Inicia el AG
Console.WriteLine("\nEjecucion Terminada \n");
Console.WriteLine("La Mejor Aproximacion a la solucion encontrada es :\n " + cadenana); //se imprime la mejor solucion encontrada
Console.ReadLine(); //esto es para hacer un pausa y se pueda ver el resultado tranquilamente
}
private void Iniciar_Click(object sender, RoutedEventArgs e)
{
Bitmap bmpDest = new Bitmap(bmpObj.Width, bmpObj.Height);
Int32[] solucion = new Int32[bmpObj.Width * bmpObj.Height];
int i = 0;
for (int y = 0; y < bmpObj.Height; y++)
{
for (int x = 0; x < bmpObj.Width; x++)
{
System.Drawing.Color color = bmpObj.GetPixel(x, y);
solucion[i] = color.ToArgb();
i++;
//bmpDest.SetPixel(x, y, System.Drawing.Color.FromArgb(acolor));
}
}
int tam = bmpObj.Width * bmpObj.Height;
double[] minArray = new double[tam];
double[] maxArray = new double[tam];
int[] bits = new int[tam];
int[] b2 = new int[tam];
for (int j = 0; j < tam; j++)
{
minArray[j] = -16777216;
maxArray[j] = -1;
bits[j] = 64;
b2[j] = 0;
}
var chromosome = new FloatingPointChromosome(
minArray,
maxArray,
bits,
b2
);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
double[] values = fc.ToFloatingPoints();
//Int32[] valuesAux = new Int32[values.Length];
//for (int b = 0; b < values.Length; b++) {
// valuesAux[b] = Convert.ToInt32(values[b]);
//}
double acum;
acum = 0;
for (int j = 0; j < values.Length; j++)
{
byte[] bA = BitConverter.GetBytes(Convert.ToInt32(values[j]));
byte[] bA2 = BitConverter.GetBytes(solucion[j]);
int ac = 0;
for (int b = 0; b < 4; b++)
{
ac += Math.Abs(bA[b] - bA2[b]);
}
acum += ac;
}
if (acum == 0)
{
return(Int32.MaxValue);
}
else
{
return(1 / acum);
}
}
);
var population = new Population(50, 100, chromosome);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.7f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(1000);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
var latestFitness = 0.0;
ga.MutationProbability = 0.3f;
ga.GenerationRan += (a, b) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Dispatcher.BeginInvoke((Action)(() =>
{
int pos = 0;
for (int y = 0; y < bmpObj.Height; y++)
{
for (int x = 0; x < bmpObj.Width; x++)
{
var aas = phenotype[pos];
bmpDest.SetPixel(x, y, System.Drawing.Color.FromArgb(Convert.ToInt32(phenotype[pos])));
pos++;
}
}
BitmapSource bs = System.Windows.Interop.Imaging.CreateBitmapSourceFromHBitmap(
bmpDest.GetHbitmap(),
IntPtr.Zero,
System.Windows.Int32Rect.Empty,
BitmapSizeOptions.FromWidthAndHeight(bmpDest.Width, bmpDest.Height));
ImageBrush ib = new ImageBrush(bs);
Destino.Source = bs;
//Resta
Bitmap resta = new Bitmap(bmpObj.Width, bmpObj.Height);
pos = 0;
for (int y = 0; y < bmpObj.Height; y++)
{
for (int x = 0; x < bmpObj.Width; x++)
{
if (phenotype[pos] - solucion[pos] != 0)
{
resta.SetPixel(x, y,
System.Drawing.Color.FromArgb(-16777216)
);
}
pos++;
}
}
BitmapSource bs2 = System.Windows.Interop.Imaging.CreateBitmapSourceFromHBitmap(
resta.GetHbitmap(),
IntPtr.Zero,
System.Windows.Int32Rect.Empty,
BitmapSizeOptions.FromWidthAndHeight(bmpDest.Width, bmpDest.Height));
RestaM.Source = bs2;
}));
}
};
Task.Run(() => {
ga.Start();
});
}
public void Start(TrainerConfig config)
{
var dirInfo = "results/" + DateTime.Now.ToString("yyyy-MM-dd-HHmmss");
logger.Info(config.ToString());
logger.Info("Start training...");
Stopwatch sw1 = new Stopwatch();
Stopwatch sw2 = new Stopwatch();
sw1.Start();
var termination = new FitnessStagnationTermination(50);
TetrisChromosome adamChromosome = initialChromosome ?? new TetrisChromosome();
GeneticAlgorithm ga = new GeneticAlgorithm(new TplPopulation(config.PopulationSize, config.PopulationSize * 2, adamChromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
}, new TetrisFitness(config.NumSample, config.MaxRound, config.UseVarianceOfFitness), new RouletteWheelSelection(), new TetrisCrossover(45, 0.8f, 0.2f), new UniformMutation())
{
OperatorsStrategy = new TplOperatorsStrategy()
};
ga.TimeEvolving.Add(initialTime);
ga.Termination = termination;
var terminationName = ga.Termination.GetType().Name;
ga.CrossoverProbability = config.CrossoverProbability;
ga.MutationProbability = config.MutationProbability;
ga.TaskExecutor = new TplTaskExecutor()
{
};
ga.GenerationRan += delegate
{
var time = sw2.Elapsed;
if (ga.Population.GenerationsNumber % 1 == 0)
{
var genResult = GenerationResult.Create(ga, time);
genResult.Save(dirInfo + "/generation_result", genResult.CreateFileName());
(ga.Population.BestChromosome as TetrisChromosome).GetParameter().Save(dirInfo, $"params-{genResult.Generation}.nnprm");
}
var bestChromosome = ga.Population.BestChromosome;
logger.Info($"Termination: {terminationName}");
logger.Info($"Generations: {ga.Population.GenerationsNumber + initialGeneration}");
logger.Info($"Fitness: {bestChromosome.Fitness}");
logger.Info($"Time:{time}");
logger.Info($"EvolvingTime: {ga.TimeEvolving}");
logger.Info($"Speed (gen/sec): {ga.Population.GenerationsNumber / ga.TimeEvolving.TotalSeconds}");
sw2.Restart();
logger.Info($"Start Generation {ga.GenerationsNumber + initialGeneration}....");
};
ga.TerminationReached += delegate
{
var total_time = sw1.Elapsed;
sw1.Stop();
sw2.Stop();
var param = (ga.Population.BestChromosome as TetrisChromosome).GetParameter();
logger.Info("Training Finished!!");
logger.Info($"TotalGeneration:{ga.GenerationsNumber}");
logger.Info($"Time:{total_time}");
logger.Info("-----Result-----");
logger.Info(param.MiddleLayerWeight);
logger.Info(param.OutputLayerWeight);
};
logger.Info($"Start Generation {ga.GenerationsNumber + initialGeneration}....");
sw2.Start();
ga.Start();
}
static void Main(string[] args)
{
float largMax = 998f; //largura maxima ou maxWidht do retângulo
float altMax = 680f; //altura maxima ou maxHeight do retângulo
var cromossomo = new FloatingPointChromosome //Criando um cromossomo de distância euclidiana
(
new double[] { 0, 0, 0, 0 },
new double[] { largMax, altMax, largMax, altMax },
new int[] { 10, 10, 10, 10 },
new int[] { 0, 0, 0, 0 }
);
var population = new Population(50, 100, cromossomo); // população, de no minimo 50 e no maximo 100, criada usando o cromossomo
var fitness = new FuncFitness((c) => // criando uma função fitness
{
var fc = c as FloatingPointChromosome;
var valores = fc.ToFloatingPoints();
var x1 = valores[0];
var y1 = valores[1];
var x2 = valores[2];
var y2 = valores[3];
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2))); //retorno
});
var peneira = new EliteSelection(); // seletor "peneira" do tipo ELITE ou seja só os melhores passarão >>>> Besides this, you can use the already implemented classic selections: Elite, Roulete Wheel, Stochastic Universal Sampling and Tournament.
var crossover = new UniformCrossover(0.5f); // aqui ocorre a união dos cromossomos para gerar novas possibilidades de soluções da proxima geração
var mutacao = new FlipBitMutation(); // classe de mutação
var terminator = new FitnessStagnationTermination(100); // essa parte passa uma "régua" no cromossomo e para de executar quando ele gera o melhor cromossomo 100 vezes.
var creator = new GeneticAlgorithm(population, fitness, peneira, crossover, mutacao); // instaciei o algoritmo genetico>>>>
creator.Termination = terminator; // parametros de terminação passados atraves do terminator
//creator.Start();// mando ele iniciar o algoritmo daqui pra baixo da pra apagar que vai funcionar
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
creator.GenerationRan += (sender, e) =>
{
var bestChromosome = creator.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Console.WriteLine(
"Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
creator.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness
);
}
};
creator.Start();
Console.ReadKey();
}
private void EvolveGeneticStrategyButton_Click(object sender, RoutedEventArgs e)
{
OutputTextBlock.Text = "Evolving...";
Task.Run(() =>
{
var chromosome = new BlackjackChromosome();
var fitness = new BlackjackFitness();
var population = new Population(Settings.GeneticSettings.MinPopulationSize, Settings.GeneticSettings.MaxPopulationSize, chromosome);
ISelection selection;
switch (Settings.GeneticSettings.SelectionType)
{
case SelectionType.Elite:
selection = new EliteSelection();
break;
case SelectionType.RouletteWheel:
selection = new RouletteWheelSelection();
break;
case SelectionType.StochasticUniversalSampling:
selection = new StochasticUniversalSamplingSelection();
break;
case SelectionType.Tournament:
selection = new TournamentSelection(Settings.GeneticSettings.TournamentSize);
break;
default:
throw new InvalidOperationException();
}
ICrossover crossover;
switch (Settings.GeneticSettings.CrossoverType)
{
case CrossoverType.AlternatingPosition:
crossover = new AlternatingPositionCrossover();
break;
case CrossoverType.CutAndSplice:
crossover = new CutAndSpliceCrossover();
break;
case CrossoverType.Cycle:
crossover = new CycleCrossover();
break;
case CrossoverType.OnePoint:
crossover = new OnePointCrossover();
break;
case CrossoverType.TwoPoint:
crossover = new TwoPointCrossover();
break;
case CrossoverType.OrderBased:
crossover = new OrderBasedCrossover();
break;
case CrossoverType.Ordered:
crossover = new OrderedCrossover();
break;
case CrossoverType.PartiallyMapped:
crossover = new PartiallyMappedCrossover();
break;
case CrossoverType.PositionBased:
crossover = new PositionBasedCrossover();
break;
case CrossoverType.ThreeParent:
crossover = new ThreeParentCrossover();
break;
case CrossoverType.Uniform:
crossover = new UniformCrossover(Settings.Current.GeneticSettings.MixProbability);
break;
case CrossoverType.VotingRecombination:
crossover = new VotingRecombinationCrossover();
break;
default:
throw new InvalidOperationException();
}
var mutation = new UniformMutation();
var termination = new FitnessStagnationTermination(Settings.Current.GeneticSettings.NumStagnantGenerations);
var taskExecutor = new ParallelTaskExecutor();
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.TaskExecutor = taskExecutor;
ga.MutationProbability = Settings.GeneticSettings.MutationProbability;
ga.CrossoverProbability = Settings.GeneticSettings.CrossoverProbability;
var latestFitness = double.MinValue;
ga.GenerationRan += (s, o) =>
{
geneticStrategy = (IStrategy)ga.BestChromosome;
var generationNumber = ga.GenerationsNumber;
var bestFitness = ga.BestChromosome.Fitness.Value;
var avgFitness = ga.Population.CurrentGeneration.Chromosomes.Average(c => c.Fitness.Value);
Dispatcher.Invoke(() =>
{
if (generationNumber == 1)
{
OutputTextBlock.Text = string.Empty;
}
OutputTextBlock.Text = $"Gen: {generationNumber}\tFit: {bestFitness}\tAvg: {avgFitness.ToString("0")}\n" + OutputTextBlock.Text;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var savedImageName = Settings.Current.GeneticSettings.SaveImagePerGeneration ? "gen" + generationNumber : null;
StrategyViewer.Draw(GeneticStrategyCanvas, geneticStrategy, $"Best from generation {generationNumber}", savedImageName);
}
}, DispatcherPriority.Background);
};
ga.TerminationReached += (s, o) =>
{
Dispatcher.Invoke(() =>
{
OutputTextBlock.Text = "Termination reached.\n" + OutputTextBlock.Text;
TestGeneticStrategyButton.IsEnabled = true;
}, DispatcherPriority.Background);
};
ga.Start();
});
}
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();
}
public void TestMethod1()
{
float maxWidth = 998f;
float maxHeight = 680f;
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 10, 10, 10, 10 },
new int[] { 0, 0, 0, 0 });
var population = new Population(50, 100, chromosome);
var fitness = new FuncFitness((c) => {
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) => {
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Trace.WriteLine(string.Format(
"Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness
));
}
};
ga.Start();
}
static void Main(string[] args)
{
float maxWidth = 998f;
float maxHeight = 680f;
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 10, 10, 10, 10 },
new int[] { 0, 0, 0, 0 });
var population = new Population(50, 100, chromosome);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
//TODO посмотреть, сколькл хромосом из общего числа выбрано
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
//TODO если фитнес равна нулю, мы ее можем записывать
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Console.WriteLine(
"Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness
);
}
};
ga.Start();
Console.ReadKey();
}
private void btnStart_Click(object sender, EventArgs e)
{
Models.Instance i = new Models.Instance();
i.Days = 7;
i.Doctors = doctors;
int min = (int)numMin.Value;
int max = (int)numMax.Value;
var chromosome = new Models.Chromosome(21, i, r);
var population = new Population(min, max, chromosome);
var fitness = new Models.Fitness();
IMutation mutation = new TworsMutation();
ISelection selection = new RouletteWheelSelection();
ICrossover crossover = new OnePointCrossover(r.Next(20));
if (cbxMutation.SelectedItem.ToString() == "Insertion")
{
mutation = new InsertionMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Partial Shuffle")
{
mutation = new PartialShuffleMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Reverse Sequence")
{
mutation = new ReverseSequenceMutation();
}
if (cbxSelection.SelectedItem.ToString() == "Elitizam")
{
selection = new EliteSelection();
}
if (cbxCrossover.SelectedItem.ToString() == "Two-point")
{
int p1 = r.Next(19);
int p2 = r.Next(p1 + 1, 20);
crossover = new TwoPointCrossover(p1, p2);
}
else if (cbxCrossover.SelectedItem.ToString() == "Uniform")
{
crossover = new UniformCrossover();
}
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ITermination termination = new FitnessStagnationTermination(50);
if (cbxTermination.SelectedItem.ToString() == "Generation number")
{
termination = new GenerationNumberTermination(200);
}
ga.Termination = termination;
ga.MutationProbability = (float)numProbability.Value;
ga.Start();
Gene[] g = ga.BestChromosome.GetGenes();
dataView.Rows.Clear();
for (int j = 0; j < 7; j++)
{
string[] row = new string[] { ((List <int>)g[j * 3].Value)[0].ToString() + " " + ((List <int>)g[j * 3].Value)[1].ToString(),
((List <int>)g[j * 3 + 1].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 1].Value)[1].ToString(),
((List <int>)g[j * 3 + 2].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 2].Value)[1].ToString() };
dataView.Rows.Add(row);
dataView.Rows[j].HeaderCell.Value = (j + 1).ToString();
}
lblFitness.Text = ga.BestChromosome.Fitness.ToString() + " , generacija broj " + ga.GenerationsNumber.ToString();
}
public static void Main(string[] args)
{
//Inicjujemy zmienne które będą granicznymi wartościami dla naszych punktów x1,x2,y1,y2.
double maxWidth = 9999;
double maxHeight = 8765;
//Tworzymy strukture pojedyńczego chormosomu
var chromosome = new FloatingPointChromosome(
new double[] { -1000, -1000, -1000, -1000 }, //Warości początkowe dla x1,x2,y1,y2
new double[] { maxWidth, maxHeight, maxWidth, maxHeight }, //Wartości maksymalne dla x1,x2,y1,y2
new int[] { 64, 64, 64, 64 }, //Liczba bitów użytych do uzyskania wyniku
new int[] { 0, 0, 0, 0 });
var population = new Population(10, 50, chromosome); //Określamy ilość chromosomów
//(x,y,chromosome) x=minimalna liczba chromosomów, y=maksymalna liczba chormosomów
//chromosome=odniesienie się do wyżej utworzonego chromosomu
var fitness = new FuncFitness((c) => //Implementacja funkcji fitness z chormosomem w zmiennej "c"
{
var fc = c as FloatingPointChromosome; //Rzutowanie powyższego chormosomu na FloatingPointChromosme
var values = fc.ToFloatingPoints(); //konwertowanie FloatingPointChromosome do jego fenotypu
var x1 = values[0]; //konwertowanie FloatingPointChromosome do jego fenotypu x1
var y1 = values[1]; //konwertowanie FloatingPointChromosome do jego fenotypu y1
var x2 = values[2]; //konwertowanie FloatingPointChromosome do jego fenotypu x2
var y2 = values[3]; //konwertowanie FloatingPointChromosome do jego fenotypu y2
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2))); //Przekazanie wartości do naszej funkcji
});
var selection = new EliteSelection(); //Selekcja odpowiada za wybór chormosomów o największym dystanisie
var crossover = new UniformCrossover(0.5f); //Ta sekcja pozwala na krzyżowanie się chromosomów aby generować nowe rozwiązania dla kolejnej generacji
//Potomstwo przy parametrze 0.5 posiada mniej więcej połowę genów rodziców.
var mutation = new FlipBitMutation(); //Mutacja uniemożliwa "utknięcie" naszego algorytmu w optimum lokalnym, mutacja Flip-Bit lodowo wybiera
//gen i odwraca jego wartości, gen o wartości 1 stanie się genem o wartości 0
var termination = new FitnessStagnationTermination(1000); //Warunek zakończenia działania algorytmu
//Jeżeli nasz program wygeneruje w ciągu 1000 pokoleń taką samą wartość funkcji fitness to działanie programu zostanie zakończone.
//Poniższy fragment odpowiada ze wywołanie zdefiniowanych przez nas wartości komenda ga.Start() spodowuje rozpoczęcie działania funkcji
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
//Cały poniższy blok kodu podowuje wyświetlenie kolejnych kroków wyliczania AG na ekranie konsoli
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Console.WriteLine(
"Generation {0,2}: (X1={1},Y1={2}),(X2={3},Y2={4}) = Największy dystans między punktami {5}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness
);
}
};
ga.Start();
Console.ReadKey();
}
public void Start()
{
var w = new StreamWriter("C:\\Users\\Taylor\\source\\repos\\ComboProject\\ComboProject\\bin\\Debug\\genelog.csv");
int gen = 1;
// init chromosomes
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
new double[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20 },
new int[] { 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 });
// gen population
var population = new Population(50, 100, chromosome);
// fitness function
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var genes = fc.ToFloatingPoints();
string combo = combogen.generateGeneticCombo(genes, 0);
int damage = ComboSimulator.getComboDamage(combo, movelist);
w.WriteLine(string.Format("{0},{1},{2},,{3},{4},{5},{6},{7},{8},{9},{10},{11},{12}",
gen.ToString(),
combo,
damage.ToString(),
genes[0],
genes[1],
genes[2],
genes[3],
genes[4],
genes[5],
genes[6],
genes[7],
genes[8],
genes[9]));
w.Flush();
return(damage);
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new ReverseSequenceMutation();
var termination = new FitnessStagnationTermination(5000);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.MutationProbability = 0.7f;
Console.WriteLine("Generation: combo = damage");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
string combo = combogen.generateGeneticCombo(bestChromosome.ToFloatingPoints(), 0);
Console.WriteLine("Generation {0,2}: {1} = {2}",
ga.GenerationsNumber,
combo,
bestFitness
);
}
gen++;
};
ga.Start();
Console.ReadKey();
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
public static DynamoGeneticAlgorithm CreateGeneticAlgorithm(Population population,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object selection,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object crossover,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object mutation,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object termination,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] int?selectionSize,
double crossoverProbability = DynamoGeneticAlgorithm.DefaultCrossoverProbability,
double mutationProbability = DynamoGeneticAlgorithm.DefaultMutationProbability)
{
IChromosome exemplaryChromome = population.CurrentGeneration.Chromosomes[0];
ChromosomeType chromosomeType = 0;
if (exemplaryChromome is BinaryChromosome)
{
chromosomeType = ((BinaryChromosome)exemplaryChromome).ChromosomeType;
}
else if (exemplaryChromome is CombinatorialChromosome)
{
chromosomeType = ((CombinatorialChromosome)exemplaryChromome).ChromosomeType;
}
else if (exemplaryChromome is DoubleChromosome)
{
chromosomeType = ((DoubleChromosome)exemplaryChromome).ChromosomeType;
}
ISelection selectionMethod = null;
if (selection == null)
{
selectionMethod = new EliteSelection();
}
else
{
if (selection is EliteSelection)
{
selectionMethod = selection as EliteSelection;
}
else if (selection is RouletteWheelSelection)
{
selectionMethod = selection as RouletteWheelSelection;
}
else if (selection is StochasticUniversalSamplingSelection)
{
selectionMethod = selection as StochasticUniversalSamplingSelection;
}
else if (selection is TournamentSelection)
{
selectionMethod = selection as TournamentSelection;
}
else
{
throw new CrossoverException("Invalid selection input. A valid object returned by a node of the Selections category should be used.");
}
}
ICrossover crossoverMethod = null;
if (crossover == null)
{
crossoverMethod = new UniformCrossover(0.5f);
}
else
{
if (crossover is AlternatingPositionCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Alternating-position Crossover (AP) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as AlternatingPositionCrossover;
}
else if (crossover is CutAndSpliceCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Cut and Splice Crossover (AP) can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as CutAndSpliceCrossover;
}
else if (crossover is CycleCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Cycle Crossover (CX) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as CycleCrossover;
}
else if (crossover is OnePointCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The One-point Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as OnePointCrossover;
}
else if (crossover is OrderBasedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Order Based Crossover (OX2) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as OrderBasedCrossover;
}
else if (crossover is OrderedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Ordered Crossover (OX1) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as OrderedCrossover;
}
else if (crossover is PartiallyMappedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Partially-mapped Crossover (PMX) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as PartiallyMappedCrossover;
}
else if (crossover is PositionBasedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Position Based Crossover (POS) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as PositionBasedCrossover;
}
else if (crossover is SimulatedBinaryCrossover)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Simulated Binary Crossover (SBX) can be only used with double chromosomes. The specified individuals are not double chromosomes.");
}
crossoverMethod = crossover as SimulatedBinaryCrossover;
}
else if (crossover is ThreeParentCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Three-parent Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as ThreeParentCrossover;
}
else if (crossover is TwoPointCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Two-point Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as TwoPointCrossover;
}
else if (crossover is UniformCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Uniform Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as UniformCrossover;
}
else if (crossover is VotingRecombinationCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Voting Recombination Crossover (VR) can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as VotingRecombinationCrossover;
}
else
{
throw new CrossoverException("Invalid crossover input. A valid object returned by a node of the Crossovers category should be used.");
}
}
IMutation mutationMethod = null;
if (mutation == null)
{
mutationMethod = new FlipBitMutation();
}
else
{
if (mutation is DisplacementMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as DisplacementMutation;
}
else if (mutation is FlipBitMutation)
{
if (chromosomeType != ChromosomeType.BinaryChromosome)
{
throw new CrossoverException("The Flip Bit Mutation can be only used on binary chromosomes. The specified individuals are not binary chromosomes.");
}
mutationMethod = mutation as FlipBitMutation;
}
else if (mutation is GaussianMutation)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Gaussian Mutation can be only used on double chromosomes. The specified individuals are not double chromosomes.");
}
mutationMethod = mutation as GaussianMutation;
}
else if (mutation is InsertionMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as InsertionMutation;
}
else if (mutation is PartialShuffleMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation (PSM) can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as PartialShuffleMutation;
}
else if (mutation is PolynomialMutation)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Polynomial Mutation can be only used on double chromosomes. The specified individuals are not double chromosomes.");
}
mutationMethod = mutation as PolynomialMutation;
}
else if (mutation is ReverseSequenceMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation (RSM) can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as ReverseSequenceMutation;
}
else if (mutation is TworsMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as TworsMutation;
}
else if (mutation is UniformMutation)
{
if (chromosomeType != ChromosomeType.BinaryChromosome)
{
throw new CrossoverException("The Uniform Mutation can be only used on binary chromosomes. The specified individuals are not binary chromosomes.");
}
mutationMethod = mutation as UniformMutation;
}
else
{
throw new CrossoverException("Invalid mutation input. A valid object returned by a node of Mutations category should be used.");
}
}
ElitistReinsertion reinsertionMethod = new ElitistReinsertion();
ITermination terminationMethod = null;
if (termination == null)
{
terminationMethod = new FitnessStagnationTermination(100);
}
else
{
if (termination is FitnessStagnationTermination)
{
terminationMethod = termination as FitnessStagnationTermination;
}
else if (termination is FitnessThresholdTermination)
{
terminationMethod = termination as FitnessThresholdTermination;
}
else if (termination is GenerationNumberTermination)
{
terminationMethod = termination as GenerationNumberTermination;
}
else if (termination is TimeEvolvingTermination)
{
terminationMethod = termination as TimeEvolvingTermination;
}
else if (termination is AndTermination)
{
terminationMethod = termination as AndTermination;
}
else if (termination is OrTermination)
{
terminationMethod = termination as OrTermination;
}
else
{
throw new CrossoverException("Invalid termination input. An object returned by nodes of Terminations library should be used.");
}
}
if (selectionSize == null)
{
selectionSize = (int)Math.Round(0.5 * population.MaxSize);
}
else if (!(selectionSize is int))
{
throw new CrossoverException("Defined selection size is not an integer.");
}
else if (selectionSize > population.MaxSize)
{
throw new CrossoverException("Selection size cannot be greater than population size.");
}
var algorithm = new DynamoGeneticAlgorithm(population, selectionMethod, crossoverMethod, mutationMethod, reinsertionMethod, terminationMethod)
{
SelectionSize = (int)selectionSize,
CrossoverProbability = (float)crossoverProbability,
MutationProbability = (float)mutationProbability,
Timer = Stopwatch.StartNew()
};
algorithm.Timer.Start();
return(algorithm);
}
public IGraphColoringResponse ColorGraph(IGraphColoringRequest graphColoringRequest)
{
try
{
Graph graph = graphColoringRequest.Graph;
GraphProvider.SetGraph(graph);
Console.WriteLine(graph.PrintGraphInOriginalForm());
CPChromosome chromosome = new CPChromosome(graphColoringRequest.StartValues.Length, graphColoringRequest.StartValues, graphColoringRequest.Population);
Population population = new Population(graphColoringRequest.PopulationSize, graphColoringRequest.PopulationSize, chromosome);
CPFitness fitness = new CPFitness();
EliteSelection selection = new EliteSelection();
OnePointCrossover crossover = new OnePointCrossover();
CPMutation mutation = new CPMutation();
FitnessStagnationTermination termination = new FitnessStagnationTermination(50);
GeneticAlgorithm ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination, MutationProbability = 0.1f
};
int latestFitness = int.MinValue;
int bestFitness = 0;
CPChromosome bestChromosome = null;
ga.GenerationRan += (sender, e) =>
{
bestChromosome = ga.BestChromosome as CPChromosome;
bestFitness = (int)-bestChromosome.Fitness.Value;
if (bestFitness == latestFitness)
{
return;
}
latestFitness = bestFitness;
bestChromosome.GetValues();
Console.WriteLine("Current Generation Result : {0}", bestFitness);
};
ga.TerminationReached += (sender, e) => { Console.WriteLine("This is the end of generations"); };
ga.Start();
Console.WriteLine("End");
if (bestFitness > graph.Vertexes.Count)
{
return(new GraphColoringResponse());
}
int[] resultGenType = bestChromosome.GetValues();
IDictionary <int, int> coloredVertex = new Dictionary <int, int>(graph.Vertexes.Count);
foreach (int vertex in graph.Vertexes)
{
coloredVertex.Add(vertex, resultGenType[vertex - 1]);
}
GraphWithColoredVertexes coloredGraph = new GraphWithColoredVertexes(graph, coloredVertex);
return(new GraphColoringResponse(coloredGraph));
}
catch (Exception ex)
{
Console.WriteLine(ex);
return(new GraphColoringResponse());
}
}
public void Optimize(bool parallel = false)
{
Console.WriteLine("Running optimization");
// Rank StopGain StopLoss MinEpsGrowth
var chromosome = new SimChromosome();
var population = new Population(25, 50, chromosome);
var fitness = new SimFitness(List, Dbfile);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
if (parallel)
{
var taskExecutor = new ParallelTaskExecutor();
taskExecutor.MinThreads = 4;
taskExecutor.MaxThreads = 12;
ga.TaskExecutor = taskExecutor;
}
ga.Termination = termination;
Console.WriteLine("Generation: (minRank, stopGain, stopLoss) = value");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as SimChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var Rank = (double)bestChromosome.GetGene(0).Value;
var StopGain = (double)bestChromosome.GetGene(1).Value;
var StopLoss = (double)bestChromosome.GetGene(2).Value;
var MinGrowth = (double)bestChromosome.GetGene(3).Value;
Console.WriteLine(
"Generation {0,2}: ({1},{2},{3}) = {4}",
ga.GenerationsNumber,
Rank,
StopGain,
StopLoss,
bestFitness
);
}
};
ga.Start();
}
public void functionOptimizationWithGeneticsharp()
{
/* example from
* http://diegogiacomelli.com.br/function-optimization-with-geneticsharp/
*/
float maxWidth = 998f;
float maxHeight = 680f;
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 12, 12, 12, 12 },
new int[] { 0, 0, 0, 0 });
var population = new Population(50, 100, chromosome);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
var selection = new RouletteWheelSelection();
var crossover = new ThreeParentCrossover();
var mutation = new PartialShuffleMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
var s = string.Format("Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness);
Console.WriteLine(
s
);
}
};
ga.Start();
}
static void Main(string[] args)
{
#region Excel Input
FileInfo InputFile = new FileInfo(@"D:\InputPLASH.xlsx");
List <double> InputPrecipUp = new List <double>();
List <double> InputPrecipDown = new List <double>();
List <double> InputQObs = new List <double>();
List <double> InputEvap = new List <double>();
using (ExcelPackage package = new ExcelPackage(InputFile))
{
ExcelWorksheet worksheet = package.Workbook.Worksheets[1];
int ColCount = worksheet.Dimension.End.Column;
int RowCount = worksheet.Dimension.End.Row;
for (int row = 2; row <= RowCount; row++)
{
InputPrecipUp.Add(Convert.ToDouble(worksheet.Cells[row, 2].Value));
InputPrecipDown.Add(Convert.ToDouble(worksheet.Cells[row, 3].Value));
InputQObs.Add(Convert.ToDouble(worksheet.Cells[row, 4].Value));
InputEvap.Add(Convert.ToDouble(worksheet.Cells[row, 5].Value));
//Console.WriteLine("PrecipUp: {0}, PrecipDown: {1}, Evap: {2}, QObos: {3}", Math.Round(InputPrecipUp[row - 2],3), Math.Round(InputPrecipDown[row - 2], 3), Math.Round(InputQObs[row - 2],3), Math.Round(InputEvap[row - 2],3));
}
}
#endregion Excel Input
#region PLASH Simulation
#region Genetic Algorithm
int SimulationLength = InputPrecipUp.Count;
double Timestep = 24;
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 2, 24, 0, //Param Upstream
0, 0, 0, 120, 6,
0, 0, 0, 0,
0, 0, 0, 0, //Initial Upstream
0, 0, 2, 24, 0, //Param Downstream
0, 0, 0, 120, 6,
0, 0, 0, 0,
0, 0, 0, 0, //Initial Downstream
12, 0.01 //Param Muskingum
},
new double[] {
10, 10, 10, 240, 300,
0.5, 0.5, 10, 3600, 120,
3, 500, 1, 1,
10, 10, 10, 10,
10, 10, 10, 240, 300,
0.5, 0.5, 10, 3600, 120,
20, 200, 1, 1,
10, 10, 10, 10,
180, 0.5
},
new int[] { 64, 64, 64, 64, 64,
64, 64, 64, 64, 64,
64, 64, 64, 64,
64, 64, 64, 64,
64, 64, 64, 64, 64,
64, 64, 64, 64, 64,
64, 64, 64, 64,
64, 64, 64, 64,
64, 64 },
new int[] { 3, 3, 3, 3, 3,
3, 3, 3, 3, 3,
3, 3, 3, 3,
3, 3, 3, 3,
3, 3, 3, 3, 3,
3, 3, 3, 3, 3,
3, 3, 3, 3,
3, 3, 3, 3,
3, 3 });
var population = new Population(50, 100, chromosome);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
if (values[12] < values[13] || values[30] < values[31])
{
return(double.NegativeInfinity);
}
DateTime[] TimeSeries = new DateTime[SimulationLength];
TimeSeries[0] = new DateTime(DateTime.Now.Year, DateTime.Now.Month, DateTime.Now.Day);
for (int i = 1; i < TimeSeries.Length; i++)
{
TimeSeries[i] = TimeSeries[0].AddHours(Timestep * i);
}
PLASHInput InputUp = new PLASHInput
{
DTE_Arr_TimeSeries = TimeSeries,
FLT_Arr_PrecipSeries = InputPrecipUp.ToArray(),
FLT_Arr_EPSeries = InputEvap.ToArray(),
FLT_Arr_QtObsSeries = InputQObs.ToArray(),
FLT_Arr_QtUpstream = new double[SimulationLength]
};
PLASHParameters ParamUp = new PLASHParameters
{
FLT_AD = 861.42252,
FLT_AI = 0.02,
FLT_AP = 0.95,
FLT_TimeStep = 24,
FLT_DI = values[0],
FLT_IP = values[1],
FLT_DP = values[2],
FLT_KSup = values[3],
FLT_CS = values[4],
FLT_CC = values[5],
FLT_CR = values[6],
FLT_PP = values[7],
FLT_KSub = values[8],
FLT_KCan = values[9],
FLT_CH = values[10],
FLT_FS = values[11],
FLT_PS = values[12],
FLT_UI = values[13]
};
PLASHInitialConditions Res0Up = new PLASHInitialConditions()
{
RImp0 = values[14],
RInt0 = values[15],
RSup0 = values[16],
RCan0 = values[17]
};
PLASHReservoir ReservoirUp = new PLASHReservoir();
PLASHOutput OutputUp = new PLASHOutput();
PLASH.Run(InputUp, ParamUp, Res0Up, ReservoirUp, OutputUp);
Muskingum Musk = new Muskingum()
{
FLT_K = values[36],
FLT_X = values[37],
FLT_Timestep = Timestep,
FLT_Arr_InputFlow = OutputUp.FLT_Arr_Qt_Calc
};
PLASHInput InputDown = new PLASHInput()
{
DTE_Arr_TimeSeries = TimeSeries,
FLT_Arr_PrecipSeries = InputPrecipDown.ToArray(),
FLT_Arr_EPSeries = InputEvap.ToArray(),
FLT_Arr_QtObsSeries = InputQObs.ToArray(),
FLT_Arr_QtUpstream = Muskingum.ProcessDamping(Musk)
};
PLASHParameters ParamDown = new PLASHParameters
{
FLT_AD = 727.8917, //Watershed Area (km2)
FLT_AI = 0.02, //Impervious Area Fraction (km2/km2)
FLT_AP = 0.95, //Pervious Area Fraction (km2/km2)
FLT_TimeStep = 24,
FLT_DI = values[18], //Maximum Impervious Detention (mm)
FLT_IP = values[19], //Maximum Interception (mm)
FLT_DP = values[20], //Maximum Pervious Detention (mm)
FLT_KSup = values[21], //Surface Reservoir Decay (h)
FLT_CS = values[22], //Soil Saturation Capacity (mm)
FLT_CC = values[23], //Field Capacity (%)
FLT_CR = values[24], //Recharge Capacity (%)
FLT_PP = values[25], //Deep Percolation (mm/h)
FLT_KSub = values[26], //Aquifer Reservoir Decay (d)
FLT_KCan = values[27], //Channel Reservoir Decay (h)
FLT_CH = values[28], //Hydraulic Conductivity (mm/h)
FLT_FS = values[29], //Soil Capilarity Factor (mm)
FLT_PS = values[30], //Soil Porosity (cm3/cm3)
FLT_UI = values[31] //Initial Moisture (cm3/cm3)
};
PLASHInitialConditions Res0Down = new PLASHInitialConditions()
{
RImp0 = values[32],
RInt0 = values[33],
RSup0 = values[34],
RCan0 = values[35]
};
PLASHReservoir ReservoirDown = new PLASHReservoir();
PLASHOutput OutputDown = new PLASHOutput();
PLASH.Run(InputDown, ParamDown, Res0Down, ReservoirDown, OutputDown);
if (ReservoirDown.FLT_Arr_ESSup.Sum() < 30 || ReservoirUp.FLT_Arr_ESSup.Sum() < 30)
{
return(double.NegativeInfinity);
}
//double objectiveNashSut = 1;
//double MeanSimulatedFlow = OutputDown.FLT_Arr_Qt_Calc.Average();
//double NashSutUpper = 0;
//double NashSutLower = 0;
//for(int i = 0; i < OutputDown.FLT_Arr_Qt_Calc.Length; i++)
//{
// NashSutUpper += Math.Pow(OutputDown.FLT_Arr_Qt_Calc[i] - InputDown.FLT_Arr_QtObsSeries[i], 2);
// NashSutLower += Math.Pow(InputDown.FLT_Arr_QtObsSeries[i] - MeanSimulatedFlow, 2);
//}
//objectiveNashSut -= (NashSutUpper / NashSutLower);
double objectiveSquareSum = 0;
for (int i = 0; i < OutputDown.FLT_Arr_Qt_Calc.Length; i++)
{
objectiveSquareSum += Math.Pow(OutputDown.FLT_Arr_Qt_Calc[i] - InputDown.FLT_Arr_QtObsSeries[i], 2);
}
//double objectiveAbsSum = 0;
//for(int i = 0; i < OutputDown.FLT_Arr_Qt_Calc.Length; i++)
//{
// objectiveAbsSum += Math.Abs(OutputDown.FLT_Arr_Qt_Calc[i] - InputDown.FLT_Arr_QtObsSeries[i]);
//}
//return objectiveAbsSum * -1;
return(objectiveSquareSum * -1);
//return objectiveNashSut;
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.3f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(250);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
//Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
Console.WriteLine("Genetic algorithm tests");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Console.WriteLine("Generation {0}: {1}",
ga.GenerationsNumber,
bestFitness);
//Console.WriteLine(
// "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
// ga.GenerationsNumber,
// phenotype[0],
// phenotype[1],
// phenotype[2],
// phenotype[3],
// bestFitness
//);
}
};
ga.Start();
Console.WriteLine("GA Over!");
#endregion Genetic Algorithm
var bestChrom = ga.BestChromosome as FloatingPointChromosome;
var bestVal = bestChrom.ToFloatingPoints();
PLASHInput InputUpstream = new PLASHInput()
{
DTE_Arr_TimeSeries = new DateTime[SimulationLength],
FLT_Arr_PrecipSeries = InputPrecipUp.ToArray(),
FLT_Arr_EPSeries = InputEvap.ToArray(),
FLT_Arr_QtObsSeries = InputQObs.ToArray(),
FLT_Arr_QtUpstream = new double[SimulationLength]
};
PLASHParameters ParametersUpstream = new PLASHParameters()
{
FLT_AD = 861.42252, //Watershed Area (km2)
FLT_AI = 0.02, //Impervious Area Fraction (km2/km2)
FLT_AP = 0.95, //Pervious Area Fraction (km2/km2)
FLT_TimeStep = 24,
//Parameters
FLT_DI = bestVal[0], //Maximum Impervious Detention (mm)
FLT_IP = bestVal[1], //Maximum Interception (mm)
FLT_DP = bestVal[2], //Maximum Pervious Detention (mm)
FLT_KSup = bestVal[3], //Surface Reservoir Decay (h)
FLT_CS = bestVal[4], //Soil Saturation Capacity (mm)
FLT_CC = bestVal[5], //Field Capacity (%)
FLT_CR = bestVal[6], //Recharge Capacity (%)
FLT_PP = bestVal[7], //Deep Percolation (mm/h)
FLT_KSub = bestVal[8], //Aquifer Reservoir Decay (d)
FLT_KCan = bestVal[9], //Channel Reservoir Decay (h)
FLT_CH = bestVal[10], //Hydraulic Conductivity (mm/h)
FLT_FS = bestVal[11], //Soil Capilarity Factor (mm)
FLT_PS = bestVal[12], //Soil Porosity (cm3/cm3)
FLT_UI = bestVal[13] //Initial Moisture (cm3/cm3)
};
InputUpstream.DTE_Arr_TimeSeries[0] = new DateTime(DateTime.Now.Year, DateTime.Now.Month, DateTime.Now.Day);
for (int i = 1; i < InputUpstream.DTE_Arr_TimeSeries.Length; i++)
{
InputUpstream.DTE_Arr_TimeSeries[i] = InputUpstream.DTE_Arr_TimeSeries[0].AddHours(ParametersUpstream.FLT_TimeStep * i);
}
PLASHReservoir ReservoirUpstream = new PLASHReservoir();
PLASHInitialConditions InitialUpstream = new PLASHInitialConditions()
{
RImp0 = bestVal[14],
RInt0 = bestVal[15],
RSup0 = bestVal[16],
RCan0 = bestVal[17]
};
PLASHOutput OutputUpstream = new PLASHOutput();
PLASH.Run(InputUpstream, ParametersUpstream, InitialUpstream, ReservoirUpstream, OutputUpstream);
PLASHInput InputDownstream = new PLASHInput()
{
DTE_Arr_TimeSeries = new DateTime[SimulationLength],
FLT_Arr_PrecipSeries = InputPrecipDown.ToArray(),
FLT_Arr_EPSeries = InputEvap.ToArray(),
FLT_Arr_QtObsSeries = InputQObs.ToArray()
};
PLASHParameters ParametersDownstream = new PLASHParameters()
{
FLT_AD = 727.8917, //Watershed Area (km2)
FLT_AI = 0.02, //Impervious Area Fraction (km2/km2)
FLT_AP = 0.95, //Pervious Area Fraction (km2/km2)
FLT_TimeStep = 24,
//Parameters
FLT_DI = bestVal[18], //Maximum Impervious Detention (mm)
FLT_IP = bestVal[19], //Maximum Interception (mm)
FLT_DP = bestVal[20], //Maximum Pervious Detention (mm)
FLT_KSup = bestVal[21], //Surface Reservoir Decay (h)
FLT_CS = bestVal[22], //Soil Saturation Capacity (mm)
FLT_CC = bestVal[23], //Field Capacity (%)
FLT_CR = bestVal[24], //Recharge Capacity (%)
FLT_PP = bestVal[25], //Deep Percolation (mm/h)
FLT_KSub = bestVal[26], //Aquifer Reservoir Decay (d)
FLT_KCan = bestVal[27], //Channel Reservoir Decay (h)
FLT_CH = bestVal[28], //Hydraulic Conductivity (mm/h)
FLT_FS = bestVal[29], //Soil Capilarity Factor (mm)
FLT_PS = bestVal[30], //Soil Porosity (cm3/cm3)
FLT_UI = bestVal[31] //Initial Moisture (cm3/cm3)
};
InputDownstream.DTE_Arr_TimeSeries[0] = new DateTime(DateTime.Now.Year, DateTime.Now.Month, DateTime.Now.Day);
for (int i = 1; i < InputDownstream.DTE_Arr_TimeSeries.Length; i++)
{
InputDownstream.DTE_Arr_TimeSeries[i] = InputDownstream.DTE_Arr_TimeSeries[0].AddHours(ParametersDownstream.FLT_TimeStep * i);
}
PLASHReservoir ReservoirDownstream = new PLASHReservoir();
PLASHInitialConditions InitialDownstream = new PLASHInitialConditions()
{
RImp0 = bestVal[32],
RInt0 = bestVal[33],
RSup0 = bestVal[34],
RCan0 = bestVal[35]
};
PLASHOutput OutputDownstream = new PLASHOutput();
Muskingum DampenedUpstream = new Muskingum()
{
FLT_K = bestVal[36],
FLT_X = bestVal[37],
FLT_Timestep = 24,
FLT_Arr_InputFlow = OutputUpstream.FLT_Arr_Qt_Calc
};
DampenedUpstream.FLT_Arr_OutputFlow = Muskingum.ProcessDamping(DampenedUpstream);
InputDownstream.FLT_Arr_QtUpstream = DampenedUpstream.FLT_Arr_OutputFlow;
PLASH.Run(InputDownstream, ParametersDownstream, InitialDownstream, ReservoirDownstream, OutputDownstream);
Console.ReadKey();
//Console.WriteLine("");
//Console.ReadKey();
#endregion PLASH Simulation
#region Buwo Simulation
List <Buildup_Washoff> UsesUpstream = Buildup_Washoff.BuwoUpstreamList(Timestep, ReservoirUpstream.FLT_Arr_ESSup);
List <Buildup_Washoff> UsesDownstream = Buildup_Washoff.BuwoDownstreamList(Timestep, ReservoirDownstream.FLT_Arr_ESSup);
foreach (Buildup_Washoff Use in UsesUpstream)
{
Buildup_Washoff.fncBuildupWashoffProcess(Use);
}
Buildup_Washoff BuwoUpstream = Buildup_Washoff.AggregateUses(UsesUpstream, 863.178D);
foreach (Buildup_Washoff Use in UsesDownstream)
{
Buildup_Washoff.fncBuildupWashoffProcess(Use);
}
Buildup_Washoff BuwoDownstream = Buildup_Washoff.AggregateUses(UsesDownstream, 729.018D);
Buildup_Washoff Aggregate = Buildup_Washoff.Transport(BuwoUpstream, BuwoDownstream);
#endregion Buwo Simulation
//#region Excel Output
using (ExcelPackage excel = new ExcelPackage())
{
excel.Workbook.Worksheets.Add("Param_PLASHUp");
excel.Workbook.Worksheets.Add("Param_PLASHDown");
excel.Workbook.Worksheets.Add("PLASHReservoirUp");
excel.Workbook.Worksheets.Add("PLASHReservoirDown");
excel.Workbook.Worksheets.Add("PLASHInitialUp");
excel.Workbook.Worksheets.Add("PLASHInitialDown");
excel.Workbook.Worksheets.Add("Results_PLASHUp");
excel.Workbook.Worksheets.Add("Results_PLASHDown");
excel.Workbook.Worksheets.Add("Param_Muskingum");
excel.Workbook.Worksheets.Add("Results_Muskingum");
excel.Workbook.Worksheets.Add("Results_BuWo");
#region Parameters
var HeaderRowPLASHParam = new List <string[]>()
{
new string[]
{
"DI", "IP", "DP", "KSup", "CS", "CC", "CR", "PP", "Ksub", "KCan", "CH", "FS", "PS", "UI"
}
};
string headerRangePLASHParam = "A1:" + Char.ConvertFromUtf32(HeaderRowPLASHParam[0].Length + 64) + 1;
var worksheet = excel.Workbook.Worksheets["Param_PLASHUp"];
worksheet.Cells[headerRangePLASHParam].LoadFromArrays(HeaderRowPLASHParam);
List <object[]> cellDataPLASHParamUP = new List <object[]>();
cellDataPLASHParamUP.Add(new object[]
{
ParametersUpstream.FLT_DI, ParametersUpstream.FLT_IP, ParametersUpstream.FLT_DP, ParametersUpstream.FLT_KSup, ParametersUpstream.FLT_CS,
ParametersUpstream.FLT_CC, ParametersUpstream.FLT_CR, ParametersUpstream.FLT_PP, ParametersUpstream.FLT_KSub, ParametersUpstream.FLT_KCan,
ParametersUpstream.FLT_CH, ParametersUpstream.FLT_FS, ParametersUpstream.FLT_PS, ParametersUpstream.FLT_UI
});
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHParamUP);
worksheet = excel.Workbook.Worksheets["Param_PLASHDown"];
worksheet.Cells[headerRangePLASHParam].LoadFromArrays(HeaderRowPLASHParam);
List <object[]> cellDataPLASHParamDown = new List <object[]>();
cellDataPLASHParamDown.Add(new object[]
{
ParametersDownstream.FLT_DI, ParametersDownstream.FLT_IP, ParametersDownstream.FLT_DP, ParametersDownstream.FLT_KSup, ParametersDownstream.FLT_CS,
ParametersDownstream.FLT_CC, ParametersDownstream.FLT_CR, ParametersDownstream.FLT_PP, ParametersDownstream.FLT_KSub, ParametersDownstream.FLT_KCan,
ParametersDownstream.FLT_CH, ParametersDownstream.FLT_FS, ParametersDownstream.FLT_PS, ParametersDownstream.FLT_UI
});
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHParamDown);
#endregion Parameters
#region Reservoir
var HeaderRowPLASHreservoir = new List <string[]>()
{
new string[]
{
"ImpRes", "ImpEvap", "ImpFlow",
"IntRes", "IntEvap", "IntFlow",
"SurfRes", "SurfEvap", "SurfFlow",
"Inf", "InfCum", "IAE", "TP", "IAEAdim", "TPAdim",
"SoilRes", "SoilEvap", "SoilUpFlow", "SoilDownFlow",
"AquiRes", "AquiPerc", "AquiFlow",
"ChanRes", "ChanEvap", "ChanUpFlow", "ChanDownFlow"
}
};
string HeaderRangePLASHReservoir = "A1:" + Char.ConvertFromUtf32(HeaderRowPLASHreservoir[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["PLASHReservoirUp"];
worksheet.Cells[HeaderRangePLASHReservoir].LoadFromArrays(HeaderRowPLASHreservoir);
List <object[]> cellDataPLASHResUp = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataPLASHResUp.Add(new object[]
{
ReservoirUpstream.FLT_Arr_RImp[i], ReservoirUpstream.FLT_Arr_ERImp[i], ReservoirUpstream.FLT_Arr_ESImp[i],
ReservoirUpstream.FLT_Arr_RInt[i], ReservoirUpstream.FLT_Arr_ERInt[i], ReservoirUpstream.FLT_Arr_ESInt[i],
ReservoirUpstream.FLT_Arr_RSup[i], ReservoirUpstream.FLT_Arr_ERSup[i], ReservoirUpstream.FLT_Arr_ESSup[i],
ReservoirUpstream.FLT_Arr_Infiltration[i], ReservoirUpstream.FLT_Arr_Infiltration_Cumulative[i], ReservoirUpstream.FLT_Arr_IAE[i], ReservoirUpstream.FLT_Arr_TP[i], ReservoirUpstream.FLT_Arr_IAEAdim[i], ReservoirUpstream.FLT_Arr_TPAdim[i],
ReservoirUpstream.FLT_Arr_RSol[i], ReservoirUpstream.FLT_Arr_ERSol[i], ReservoirUpstream.FLT_Arr_EESol[i], ReservoirUpstream.FLT_Arr_ESSol[i],
ReservoirUpstream.FLT_Arr_RSub[i], ReservoirUpstream.FLT_Arr_PPSub[i], ReservoirUpstream.FLT_Arr_EESub[i],
ReservoirUpstream.FLT_Arr_RCan[i], ReservoirUpstream.FLT_Arr_ERCan[i], ReservoirUpstream.FLT_Arr_EECan[i], ReservoirUpstream.FLT_ARR_ESCan[i]
});
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHResUp);
worksheet = excel.Workbook.Worksheets["PLASHReservoirDown"];
worksheet.Cells[HeaderRangePLASHReservoir].LoadFromArrays(HeaderRowPLASHreservoir);
List <object[]> cellDataPLASHResDown = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataPLASHResDown.Add(new object[]
{
ReservoirDownstream.FLT_Arr_RImp[i], ReservoirDownstream.FLT_Arr_ERImp[i], ReservoirDownstream.FLT_Arr_ESImp[i],
ReservoirDownstream.FLT_Arr_RInt[i], ReservoirDownstream.FLT_Arr_ERInt[i], ReservoirDownstream.FLT_Arr_ESInt[i],
ReservoirDownstream.FLT_Arr_RSup[i], ReservoirDownstream.FLT_Arr_ERSup[i], ReservoirDownstream.FLT_Arr_ESSup[i],
ReservoirDownstream.FLT_Arr_Infiltration[i], ReservoirDownstream.FLT_Arr_Infiltration_Cumulative[i], ReservoirDownstream.FLT_Arr_IAE[i], ReservoirDownstream.FLT_Arr_TP[i], ReservoirDownstream.FLT_Arr_IAEAdim[i], ReservoirDownstream.FLT_Arr_TPAdim[i],
ReservoirDownstream.FLT_Arr_RSol[i], ReservoirDownstream.FLT_Arr_ERSol[i], ReservoirDownstream.FLT_Arr_EESol[i], ReservoirDownstream.FLT_Arr_ESSol[i],
ReservoirDownstream.FLT_Arr_RSub[i], ReservoirDownstream.FLT_Arr_PPSub[i], ReservoirDownstream.FLT_Arr_EESub[i],
ReservoirDownstream.FLT_Arr_RCan[i], ReservoirDownstream.FLT_Arr_ERCan[i], ReservoirDownstream.FLT_Arr_EECan[i], ReservoirDownstream.FLT_ARR_ESCan[i]
});
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHResDown);
#endregion Reservoir
#region Initial Conditions
var HeaderRowPLASHInitial = new List <string[]>()
{
new string[] { "RImp0", "RInt0", "RSup0", "RCan0" }
};
string headerRangePLASHInitial = "A1:" + Char.ConvertFromUtf32(HeaderRowPLASHInitial[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["PLASHInitialUp"];
worksheet.Cells[headerRangePLASHInitial].LoadFromArrays(HeaderRowPLASHInitial);
List <object[]> cellDataPLASHInitialUp = new List <object[]>();
cellDataPLASHInitialUp.Add(new object[] { InitialUpstream.RImp0, InitialUpstream.RInt0, InitialUpstream.RSup0, InitialUpstream.RCan0 });
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHInitialUp);
worksheet = excel.Workbook.Worksheets["PLASHInitialDown"];
worksheet.Cells[headerRangePLASHInitial].LoadFromArrays(HeaderRowPLASHInitial);
List <object[]> cellDataPLASHInitialDown = new List <object[]>();
cellDataPLASHInitialDown.Add(new object[] { InitialDownstream.RImp0, InitialDownstream.RInt0, InitialDownstream.RSup0, InitialDownstream.RCan0 });
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHInitialDown);
#endregion Initial Conditions
#region Results
//PLASH Upstream
var HeaderRowPLASH = new List <string[]>()
{
new string[] { "Precipitation", "Evapotranspiration", "Observed Flow",
"Impervious Reservoir", "Interception Reservoir", "Surface Reservoir", "Soil Reservoir", "Aquifer Reservoir", "Channel Reservoir",
"Calculated Basic Flow", "Calculated Surface Flow", "Calculated Total Flow" },
};
string headerRangePLASH = "A1:" + Char.ConvertFromUtf32(HeaderRowPLASH[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["Results_PLASHUp"];
worksheet.Cells[headerRangePLASH].LoadFromArrays(HeaderRowPLASH);
List <object[]> cellDataPLASHUp = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataPLASHUp.Add(new object[] { InputUpstream.FLT_Arr_PrecipSeries[i], InputUpstream.FLT_Arr_EPSeries[i], InputUpstream.FLT_Arr_QtObsSeries[i],
ReservoirUpstream.FLT_Arr_RImp[i], ReservoirUpstream.FLT_Arr_RInt[i], ReservoirUpstream.FLT_Arr_RSup[i], ReservoirUpstream.FLT_Arr_RSol[i], ReservoirUpstream.FLT_Arr_RSub[i], ReservoirUpstream.FLT_Arr_RCan[i],
OutputUpstream.FLT_Arr_QBas_Calc[i], OutputUpstream.FLT_Arr_QSup_Calc[i], OutputUpstream.FLT_Arr_Qt_Calc[i] });
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHUp);
//PLASH Downstream
worksheet = excel.Workbook.Worksheets["Results_PLASHDown"];
worksheet.Cells[headerRangePLASH].LoadFromArrays(HeaderRowPLASH);
List <object[]> cellDataPLASHDown = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataPLASHDown.Add(new object[] { InputDownstream.FLT_Arr_PrecipSeries[i], InputDownstream.FLT_Arr_EPSeries[i], InputDownstream.FLT_Arr_QtObsSeries[i],
ReservoirDownstream.FLT_Arr_RImp[i], ReservoirDownstream.FLT_Arr_RInt[i], ReservoirDownstream.FLT_Arr_RSup[i], ReservoirDownstream.FLT_Arr_RSol[i], ReservoirDownstream.FLT_Arr_RSub[i], ReservoirDownstream.FLT_Arr_RCan[i],
OutputDownstream.FLT_Arr_QBas_Calc[i], OutputDownstream.FLT_Arr_QSup_Calc[i], OutputDownstream.FLT_Arr_Qt_Calc[i] });
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataPLASHDown);
#endregion Results
#region Muskingum Parameters
//Muskingum
var HeaderRowMuskingumParam = new List <string[]>()
{
new string[] { "K", "X" },
};
string headerRangeMuskingumParam = "A1:" + Char.ConvertFromUtf32(HeaderRowMuskingumParam[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["Param_Muskingum"];
worksheet.Cells[headerRangeMuskingumParam].LoadFromArrays(HeaderRowMuskingumParam);
List <object[]> cellDataMuskingumParam = new List <object[]>();
cellDataMuskingumParam.Add(new object[] { DampenedUpstream.FLT_K, DampenedUpstream.FLT_X });
worksheet.Cells[2, 1].LoadFromArrays(cellDataMuskingumParam);
#endregion Muskingum Parameters
#region Muskingum
var HeaderRowMuskingum = new List <string[]>()
{
new string[] { "Upstream Flow", "Downstream Flow" },
};
string headerRangeMuskingum = "A1:" + Char.ConvertFromUtf32(HeaderRowMuskingum[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["Results_Muskingum"];
worksheet.Cells[headerRangeMuskingum].LoadFromArrays(HeaderRowMuskingum);
List <object[]> cellDataMuskingum = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataMuskingum.Add(new object[] { DampenedUpstream.FLT_Arr_InputFlow[i], DampenedUpstream.FLT_Arr_OutputFlow[i] });
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataMuskingum);
#endregion Muskingum
#region BuWo
//Buwo
var HeaderRow2 = new List <string[]>()
{
new string[] { "Precipitation", "Surface Flow", "Buildup", "Effective Washoff" },
};
string headerRange2 = "A1:" + Char.ConvertFromUtf32(HeaderRow2[0].Length + 64) + 1;
worksheet = excel.Workbook.Worksheets["Results_BuWo"];
worksheet.Cells[headerRange2].LoadFromArrays(HeaderRow2);
List <object[]> cellDataBuwo = new List <object[]>();
for (int i = 0; i < SimulationLength; i++)
{
cellDataBuwo.Add(new object[] { InputUpstream.FLT_Arr_PrecipSeries[i], ReservoirDownstream.FLT_Arr_ESSup[i], Aggregate.FLT_Arr_Buildup[i], Aggregate.FLT_Arr_EffectiveWashoff[i] });
}
worksheet.Cells[2, 1].LoadFromArrays(cellDataBuwo);
#endregion BuWo
FileInfo excelFile = new FileInfo(@"D:\dataGA.xlsx");
excel.SaveAs(excelFile);
}
////Console.WriteLine("Excel processed");
//#endregion Excel Output
//Console.ReadKey();
}