public static void display(Population p, int gen)
{
Tour best = p.findBest();
System.Console.WriteLine("Generation {0}\n" +
"Best fitness: {1}\n" +
"Best distance: {2}\n", gen, best.fitness, best.distance);
}
public Resource(ResourceType type, ResourceLevel level)
{
Type = type;
Level = level;
Population = new Population();
StorageLevel = 20;
}
static Population AdvanceGeneration(Population population, ISelection selection, ICrossover crossover, IMutation mutation)
{
var chromosomes = new List<Chromosome>();
population = new Population(population.Take((int)(truncationRate * population.Count()))); // TRUNCATION
chromosomes.AddRange(population.Take((int)(elitismRate * chromosomeCount))); //ELITE (assuming that the chromosomes in the population are sorted by fitness (the fitter are at the top of the list)
do
{
Chromosome chosen1 = selection.Select(population),
chosen2 = selection.Select(population);
if (random.NextDouble() < crossoverRate)
{
var children = crossover.Crossover(chosen1, chosen2); // CROSSOVER
chosen1 = children.Item1;
chosen2 = children.Item2;
}
if (random.NextDouble() < mutationRate)
{
chosen1 = mutation.Mutate(chosen1); // MUTATION
}
if (random.NextDouble() < mutationRate)
{
chosen2 = mutation.Mutate(chosen2); // MUTATION
}
chromosomes.Add(chosen1);
chromosomes.Add(chosen2);
} while (chromosomes.Count < chromosomeCount);
return new Population(chromosomes);
}
public void LoadAll(Population pop,float curHouses)
{
LoadAllHouses(curHouses);
LoadAllPeople(pop);
LoadAllBiomes();
}
public override void Select(Population population, SelectionBuffer selection, GeneticAlgorithm.NextStepDelegate callback)
{
int size = 0;
// TODO: Implement settings for how to truncate.
float cutoff = 0;
switch (truncationMode) {
case TruncationMode.AboveMiddle:
cutoff = population.MinFitness + (population.MaxFitness - population.MinFitness)/2;
break;
case TruncationMode.AboveMean:
cutoff = population.MeanFitness;
break;
case TruncationMode.AboveMedian:
cutoff = population.MedianFitness;
break;
}
for (int i = 0; i < population.Size; i++) {
if (population[i].Fitness > cutoff) {
selection[size].CloneFrom(population[i].Genome);
size++;
}
}
selection.Size = size;
callback();
}
public Population AdvancePopulation(Population population)
{
var chromosomes = new List<Chromosome>();
population = new Population(population.Take((int)(TruncationRate * population.Count()))); // TRUNCATION
do
{
Chromosome chosen1 = selection.Select(population),
chosen2 = selection.Select(population);
if (random.NextDouble() < CrossoverRate)
{
var children = crossover.Crossover(chosen1, chosen2); // CROSSOVER
chosen1 = children.Item1;
chosen2 = children.Item2;
}
if (random.NextDouble() < MutationRate)
{
chosen1 = mutation.Mutate(chosen1); // MUTATION
}
if (random.NextDouble() < MutationRate)
{
chosen2 = mutation.Mutate(chosen2); // MUTATION
}
chromosomes.Add(chosen1);
chromosomes.Add(chosen2);
} while (chromosomes.Count < ChromosomeCount);
return new Population(chromosomes);
}
public override void FitnessFunction(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
this.population = population;
InitiateNewSimulation();
simulating = true;
doneCallback = callback;
}
public PopulationSimulator(int width, int height)
{
swarmInBirthOrder = new Species();
swarmInXOrder = new Species();
swarmInYOrder = new Species();
Population = new Population();
}
private void RunGA(){
_teams = LoadTeams();
_winners = _teams.FindAll(l => l.Winner == true);
_losers = _teams.FindAll(l => l.Winner == false);
const double crossoverProbability = 0.85;
const double mutationProbability = 0.15;
const int elitismPercentage = 5;
var population = new Population(1000, 72, true, false, ParentSelectionMethod.TournamentSelection);
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.DoublePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
var ga = new GeneticAlgorithm(population, EvaluateFitness);
ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
ga.Run(TerminateAlgorithm);
}
public void InitializePanelWithTrainerData() {
DebugBot.DebugFunctionCall("TPopUI; InitializePanelWithTrainerData(); ", debugFunctionCalls);
Trainer trainer = trainerModuleScript.gameController.masterTrainer;
if(trainer.PlayerList != null) {
int curPlayer = trainer.CurPlayer;
//Debug.Log ("InitializePanelWithTrainerData(), " + trainer.PlayerList[curPlayer-1].maxMaxPopulationSize.ToString());
//sliderMaxPopulationSize.minValue = trainer.PlayerList[curPlayer-1].minMaxPopulationSize;
//sliderMaxPopulationSize.maxValue = trainer.PlayerList[curPlayer-1].maxMaxPopulationSize;
//sliderMaxPopulationSize.value = trainer.PlayerList[curPlayer-1].maxPopulationSize;
if(trainer.PlayerList[curPlayer-1].masterPopulation != null) { // if the current player has a Population instance:
populationRef = trainer.PlayerList[curPlayer-1].masterPopulation; // get current population instance
//Current Population text:
textCurrentPopulationSize.text = "Current Population Size: " + (populationRef.isFunctional ? populationRef.masterAgentArray.Length.ToString() : "0"); // Update this later!!
//Current Max Population Size:
sliderMaxPopulationSize.minValue = minMaxPopulationSize; // set up slider bounds
sliderMaxPopulationSize.maxValue = maxMaxPopulationSize;
sliderMaxPopulationSize.value = populationRef.populationMaxSize;
textMaxPopulationSize.text = populationRef.populationMaxSize.ToString();
}
else { // Population hasn't been created yet:
//textMaxPopulationSize.text = trainer.PlayerList[curPlayer-1].maxPopulationSize.ToString();
}
}
valuesChanged = false;
applyPressed = false;
UpdateUIWithCurrentData();
}
public DemoPlayerInputStrategy()
{
var population = new Population (6, 6, new DemoPlayerChromosome ());
var fitness = new DemoPlayerFitness ();
var selection = new EliteSelection ();
var crossover = new OnePointCrossover (0);
var mutation = new UniformMutation (true);
m_ga = new GeneticAlgorithm (
population,
fitness,
selection,
crossover,
mutation);
m_ga.MutationProbability = 0.5f;
m_ga.GenerationRan += (sender, e) => {
m_bestChromossome = m_ga.BestChromosome as DemoPlayerChromosome;
if(m_bestChromossome.AvoidAliensProjectilesProbability > 0.9f) {
HorizontalDirection = 1f;
}
};
m_ga.Start ();
SHThread.PingPong (.01f, 0, 1, (t) => {
m_ga.Termination = new GenerationNumberTermination (m_ga.GenerationsNumber + 1);
m_ga.Resume();
return true;
});
}
void Awake()
{
if(master == null){
master = this;
} else if (master != this){
Destroy(gameObject);
}
}
// string name,
// float fullFoodAmt,
// float feedIntSec,
// float hungryWindow,
// float energyProduceVal
public override Organism Create(Population population)
{
return new Organism(population,
"fish",
1f,
3f,
3f,
5f);
}
// string name,
// float fullFoodAmt,
// float feedIntSec,
// float hungryWindow,
// float energyProduceVal
public override Organism Create(Population population)
{
return new Organism(population,
"bacteria",
1f,
20f,
20f,
1f);
}
public Parametres(Population oldParam)
{
InitializeComponent();
MaxNotes = oldParam.MaxNotes;
MaxIndividus = oldParam.MaxIndividus;
Crossover = oldParam.Crossover;
Mutarate = oldParam.Mutarate;
}
public void InitializePanelWithTrainerData() {
Player currentPlayer = trainerModuleScript.gameController.masterTrainer.PlayerList[trainerModuleScript.gameController.masterTrainer.CurPlayer-1];
populationRef = currentPlayer.masterPopulation;
DebugBot.DebugFunctionCall("LoadPopulationUI; InitializePanelWithTrainerData(); ", debugFunctionCalls);
UpdateUIWithCurrentData();
}
// override this
public virtual Organism Create(Population population)
{
return new Organism(population,
"unnamed",
0f,
0f,
0f,
0f);
}
public void SetUp()
{
PopulationGenerator = new Mock<IPopulationGenerator<string>>();
FitnessEvaluator = new Mock<IFitnessEvaluator<string>>();
Count = 1;
PopulationMock = new Mock<Population<string>>(Count, PopulationGenerator.Object, FitnessEvaluator.Object);
Population = new Population<string>(Count, PopulationGenerator.Object, FitnessEvaluator.Object);
}
public Agent(Population populationIn, int generationIn)
{
// Debug.Log("Agent created " + Time.time);
componentsList = new ArrayList();
generation = generationIn;
population = populationIn;
CreateRandomComponents();
}
private void Scale(Population population)
{
foreach (var chromosome in population.Chromosomes)
{
double value = chromosome.Value + (population.AvgFitness - c * _sigma);
if (value < 0) value = 0;
chromosome.Value = value;
}
}
public virtual void FitnessFunctionSimple(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
for (int i = 0; i < population.Size; i++) {
PhenomeDescription pd = population[i];
pd.Fitness = CalculateFitness(pd.Genome);
}
CalculateMinMaxTotal(population);
callback();
}
public override void FitnessFunction(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
for (int i = 0; i < population.Size; i++) {
PhenomeDescription pd = population[i];
pd.Fitness = CalculateFitness(pd.Genome as BaseGenomeBinary);
}
CalculateMinMaxTotal(population);
callback();
}
protected internal override void InitializeNewRun()
{
base.InitializeNewRun();
rule = (GARule)LearningConnections.ItemArray[0].Rule;
population = new Population(rule.PopulationSize);
bestSel = new GaussianSelectionAlgorithm(rule.BestSelectStdDev);
worstSel = new GaussianSelectionAlgorithm(rule.WorstSelectStdDev, SelectionDirection.FromBottom);
nextStep = null;
}
private void CalculateSigma(Population population)
{
double avg = population.AvgFitness;
double sum = 0;
foreach(var chromosome in population.Chromosomes)
sum += Math.Pow(chromosome.Value - avg ,2);
_sigma = Math.Sqrt(sum / population.Count);
}
public static void Write(XmlNode parentNode, Population p, IActivationFunction activationFn)
{
XmlElement xmlPopulation = XmlUtilities.AddElement(parentNode, "population");
XmlUtilities.AddAttribute(xmlPopulation, "activation-fn-id", activationFn.FunctionId);
foreach(IGenome genome in p.GenomeList)
{
genome.Write(xmlPopulation);
}
}
public static SaveSpecies GetPopulationAsSaveSpecies(Population population)
{
SaveSpecies saveSpecies = new SaveSpecies();
saveSpecies.CreadtedDt = DateTime.Now;
foreach (Species species in population)
{
saveSpecies.SavedSpecies.Add(GetSavedGenomes(species));
}
return saveSpecies;
}
public void GeneratePopulationSectors(ulong size) {
//!@ ToDo: parse types from assembly
Type[] feature_types = new Type[] {
typeof(Male),
typeof(Female),
typeof(Asian),
typeof(Indian),
typeof(Jobless),
typeof(Retired),
typeof(Disable),
typeof(Artist),
typeof(Engeneer),
typeof(Child),
typeof(Teenager),
typeof(Adult),
typeof(Senior)
};
Sections = new Population[] {
new Population(123, typeof(Male), typeof(Asian), typeof(Engeneer), typeof(Adult)),
new Population(321, typeof(Female), typeof(Asian), typeof(Engeneer), typeof(Adult)),
new Population(0, typeof(Pregnant), typeof(Asian), typeof(Engeneer), typeof(Adult)),
new Population(0, typeof(Male), typeof(Asian), typeof(Jobless), typeof(Child)),
new Population(0, typeof(Female), typeof(Asian), typeof(Jobless), typeof(Child))
};
Sections[1].Sex.AddTarget(Sections[2] as ISectionSize<ulong>, Feature.Targets.Pregnant);
Sections[2].Sex.AddTarget(Sections[1] as ISectionSize<ulong>, Feature.Targets.Female);
Sections[2].Sex.AddTarget(Sections[4] as ISectionSize<ulong>, Feature.Targets.FemaleChild);
Sections[2].Sex.AddTarget(Sections[3] as ISectionSize<ulong>, Feature.Targets.MaleChild);
//!@ test
//var population = new List<Population>();
//for (int i = 0; i < 2; i++) {
// for (int j = 2; j < 4; j++) {
// for (int k = 4; k < 9; k++) {
// for (int t = 9; t < 13; t++) {
// population.Add(
// new Population(
// 1,
// Activator.CreateInstance(feature_types[i]) as Gender,
// Activator.CreateInstance(feature_types[j]) as Nationality,
// Activator.CreateInstance(feature_types[k]) as EmpStatus,
// Activator.CreateInstance(feature_types[t]) as AgeGroup)
// );
// }
// }
// }
//}
//Sections = population.ToArray();
}
public void evolveNextGeneration()
{
// save data before evolving
this.saveTxtFile ();
this.pop = Algorithm.evolvePopulation(this.pop);
this.championFitness = this.pop.getGenome (0).fitness;
this.generationCount++;
this.index = 0;
testNextGenome ();
}
public void InitializePanelWithTrainerData() {
Player currentPlayer = trainerModuleScript.gameController.masterTrainer.PlayerList[trainerModuleScript.gameController.masterTrainer.CurPlayer-1];
populationRef = currentPlayer.masterPopulation;
DebugBot.DebugFunctionCall("TNewPopUI; InitializePanelWithTrainerData(); ", debugFunctionCalls);
sliderPopulationSize.minValue = minMaxPopulationSize;
sliderPopulationSize.maxValue = maxMaxPopulationSize;
UpdateUIWithCurrentData();
}
private string createIndividualName(Population population, int individualId, IndividualExtractionOptions options)
{
var proposedName = options.GenerateIndividualName(population.Name, individualId);
return(_containerTask.CreateUniqueName(_buildingBlockRepository.All <Individual>(), proposedName, canUseBaseName: true));
}
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();
}
protected override IList <GeneticEntity> GetEliteGeneticEntitiesCore(Population population)
{
GetEliteGeneticEntitiesCoreCalled = true;
return(base.GetEliteGeneticEntitiesCore(null));
}
private void AssignID(Population pop)
{
pop.setID(openID.Pop());
pops.Add(pop);
}
public static bool Terminate(Population population, int currentGeneration, long currentEvaluation)
{
return(currentGeneration > 200);
}
public static void Box(Settings settings, Population pop, Random rand, double popLeft, double popWidth, Color color, Position position, BoxFormat boxFormat, HorizontalAlignment errorAlignment = HorizontalAlignment.Right)
{
// adjust edges to accomodate special positions
if (position == Position.Hide)
{
return;
}
if (position == Position.Left || position == Position.Right)
{
popWidth /= 2;
}
if (position == Position.Right)
{
popLeft += popWidth;
}
double errorMaxPx, errorMinPx;
double yPxTop, yPxBase;
double yPx;
if (boxFormat == BoxFormat.StdevStderrMean)
{
errorMaxPx = settings.GetPixelY(pop.mean + pop.stDev);
errorMinPx = settings.GetPixelY(pop.mean - pop.stDev);
yPxTop = settings.GetPixelY(pop.mean + pop.stdErr);
yPxBase = settings.GetPixelY(pop.mean - pop.stdErr);
yPx = settings.GetPixelY(pop.mean);
}
else if (boxFormat == BoxFormat.OutlierQuartileMedian)
{
errorMaxPx = settings.GetPixelY(pop.maxNonOutlier);
errorMinPx = settings.GetPixelY(pop.minNonOutlier);
yPxTop = settings.GetPixelY(pop.Q3);
yPxBase = settings.GetPixelY(pop.Q1);
yPx = settings.GetPixelY(pop.median);
}
else
{
throw new NotImplementedException();
}
// make cap width a fraction of available space
double capWidthFrac = .38;
double capWidth = popWidth * capWidthFrac;
// contract edges slightly to encourage padding between elements
double edgePaddingFrac = 0.2;
popLeft += popWidth * edgePaddingFrac;
popWidth -= (popWidth * edgePaddingFrac) * 2;
double leftPx = settings.GetPixelX(popLeft);
double rightPx = settings.GetPixelX(popLeft + popWidth);
Pen pen = new Pen(Color.Black, 1);
Brush brush = new SolidBrush(color);
// draw the box
RectangleF rect = new RectangleF((float)leftPx, (float)yPxTop, (float)(rightPx - leftPx), (float)(yPxBase - yPxTop));
settings.gfxData.FillRectangle(brush, rect.X, rect.Y, rect.Width, rect.Height);
settings.gfxData.DrawRectangle(pen, rect.X, rect.Y, rect.Width, rect.Height);
// draw the line in the center
settings.gfxData.DrawLine(pen, rect.X, (float)yPx, rect.X + rect.Width, (float)yPx);
// determine location of errorbars and caps
double capPx1, capPx2, errorPxX;
switch (errorAlignment)
{
case HorizontalAlignment.Center:
double centerX = popLeft + popWidth / 2;
errorPxX = settings.GetPixelX(centerX);
capPx1 = settings.GetPixelX(centerX - capWidth / 2);
capPx2 = settings.GetPixelX(centerX + capWidth / 2);
break;
case HorizontalAlignment.Right:
errorPxX = settings.GetPixelX(popLeft + popWidth);
capPx1 = settings.GetPixelX(popLeft + popWidth - capWidth / 2);
capPx2 = settings.GetPixelX(popLeft + popWidth);
break;
case HorizontalAlignment.Left:
errorPxX = settings.GetPixelX(popLeft);
capPx1 = settings.GetPixelX(popLeft);
capPx2 = settings.GetPixelX(popLeft + capWidth / 2);
break;
default:
throw new NotImplementedException();
}
// draw errorbars and caps
settings.gfxData.DrawLine(pen, (float)errorPxX, (float)errorMinPx, (float)errorPxX, rect.Y + rect.Height);
settings.gfxData.DrawLine(pen, (float)errorPxX, (float)errorMaxPx, (float)errorPxX, rect.Y);
settings.gfxData.DrawLine(pen, (float)capPx1, (float)errorMinPx, (float)capPx2, (float)errorMinPx);
settings.gfxData.DrawLine(pen, (float)capPx1, (float)errorMaxPx, (float)capPx2, (float)errorMaxPx);
}
/// <summary>
/// Selects the chromosomes which will be reinserted.
/// </summary>
/// <returns>The chromosomes to be reinserted in next generation..</returns>
/// <param name="population">The population.</param>
/// <param name="offspring">The offspring.</param>
/// <param name="parents">The parents.</param>
protected override IList <IChromosome> PerformSelectChromosomes(Population population, IList <IChromosome> offspring, IList <IChromosome> parents)
{
return(offspring);
}
/// <summary>
/// Start the solver with an initial population.
/// </summary>
/// <param name="initial"></param>
public Individual <GenomeType, ProblemType, WeightType> Start(Population <GenomeType, ProblemType, WeightType> initial)
{
WeightType fitness = default(WeightType);
Population <GenomeType, ProblemType, WeightType> population = new Population <GenomeType, ProblemType, WeightType>(initial, true);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Generating population...");
// use parallelism to generate population.
#if !WINDOWS_PHONE
if (_parallel)
{
System.Threading.Tasks.Parallel.For(population.Count, _settings.PopulationSize, delegate(int i)
{
// generate new.
IGenerationOperation <GenomeType, ProblemType, WeightType> generation_operation = this.CreateGenerationOperation();
Individual <GenomeType, ProblemType, WeightType> new_individual =
generation_operation.Generate(this);
// add to population.
lock (population)
{
population.Add(new_individual);
}
// report population generation.
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Generating population{0}/{1}...", population.Count, _settings.PopulationSize);
this.ReportNew(string.Format("Generating population..."), population.Count, _settings.PopulationSize);
});
}
#endif
while (population.Count < _settings.PopulationSize)
{
// generate new.
IGenerationOperation <GenomeType, ProblemType, WeightType> generation_operation = this.CreateGenerationOperation();
Individual <GenomeType, ProblemType, WeightType> new_individual =
generation_operation.Generate(this);
// add to population.
population.Add(new_individual);
// report population generation.
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Generating population {0}/{1}...", population.Count, _settings.PopulationSize);
this.ReportNew(string.Format("Generating population..."), population.Count, _settings.PopulationSize);
}
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Done!");
// sort the initial population.
population.Sort(this, _fitness_calculator);
//Tools.Core.Output.OutputTextStreamHost.WriteLine("Average {0}",
// _fitness_calculator.AverageFitness(_problem, population));
int stagnation = 0;
// start next generation or stop.
WeightType new_fitness =
population[0].Fitness;
// is there improvement?
if (new_fitness.CompareTo(fitness) > 0)
{ // a new fitness is found!
// raise a new event here.
this.RaiseNewFittest(population[0]);
// set new fitness.
fitness = new_fitness;
fittest = population[0];
// reset the stagnation count.
stagnation = 0;
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"New Fittest {0}", fittest.ToString());
}
// start getting genetic!
int generation_count = 0;
while (generation_count < _settings.MaxGeneration &&
stagnation < _settings.StagnationCount)
{
// check if the solving has been cancelled.
if (this.Cancelled)
{
this.ReportNew(string.Format("Solver cancelled at generation {0}!", generation_count), 1, 1);
return(null);
}
if (this.Stopped)
{
this.ReportNew(string.Format("Solver stopped at generation {0}!", generation_count), 1, 1);
return(fittest);
}
// calculate the next generation.
population =
this.NextGeneration(population);
// get the population fitness.
population.Sort(this, _fitness_calculator);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"{0}->{1}", population[0].Fitness, population[population.Count - 1].Fitness);
// start next generation or stop.
new_fitness =
population[0].Fitness;
// is there improvement?
if (new_fitness.CompareTo(fitness) < 0)
{ // a new fitness is found!
// raise a new event here.
this.RaiseNewFittest(population[0]);
// set new fitness.
fitness = new_fitness;
fittest = population[0];
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"New Fittest {0}-{1} {2}", generation_count, stagnation, fittest.ToString());
// reset the stagnation count.
stagnation = 0;
// report the new fittest.
this.ReportNew(string.Format("New Fittest {0}",
fittest.ToString()), 0, 1);
}
else
{
// increase the stagnation count.
stagnation++;
}
// raise the generation count.
generation_count++;
// raise a new generation event.
this.RaiseNewGeneration(generation_count, stagnation, population);
// report the new generation.
this.ReportNew(string.Format("Generation {0}-{1} {2}",
generation_count,
stagnation,
fittest.ToString()), stagnation, _settings.StagnationCount);
if (stagnation != 0 && stagnation % 1 == 0)
{
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Generation {0}-{1} {2}", generation_count, stagnation, fittest.ToString());
}
}
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Result [{0}]:", fitness, fittest.ToString());
// report the new generation.
this.ReportNew(string.Format("Evolution finished @ generation {0}: {1}",
generation_count,
fittest.ToString()), generation_count, _settings.StagnationCount);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", System.Diagnostics.TraceEventType.Information,
"Evolution finished @ generation {0}: {1}", generation_count, fittest.ToString());
return(fittest);
}
private string BuildSeriesId(Population population, County county)
{
return(County_Codes[county.CountyName].ToString());
}
public void ExtractIndividualsFrom(Population population, IndividualExtractionOptions individualExtractionOptions)
{
_executionContext.AddToHistory(ExtractIndividualsFromPopulationCommand(population, individualExtractionOptions));
}
public void ExtractIndividualsFrom(Population population, IEnumerable <int> individualIds)
{
ExtractIndividualsFrom(population, new IndividualExtractionOptions {
IndividualIds = individualIds
});
}
protected override IList <GeneticEntity> GetEliteGeneticEntitiesCore(Population population)
{
this.GetElitistGeneticEntitiesCallCount++;
return(base.GetEliteGeneticEntitiesCore(population));
}
public void SetPopulation(Population population, int requestedChromosomes)
{
this.population = population.GetBestChromosomes((int)Math.Ceiling(population.Count() * percentage));
}
private static void OnLifecycleFinishedEvent(Population currentPopulation)
{
Console.WriteLine($"Highest fitness: {currentPopulation.MaxFitness}");
}
/// <summary>
/// This method is will be called once per generation (after the MutationProbability method for that generation), so you can use it to remember old data.
/// </summary>
public void AddGeneration(Population population)
{
previousConvergence = GetConvergence(population.GetEvaluations());
}
{ public bool isDuplicateKey(Population pop)
{
return(pop.Name == this.Name && pop != this);
}
///<summary>
///Remove a population from the RPM
///</summary>
public void RemovePopulation(Population pop)
{
pops.Remove(pop);
openID.Push(pop.getID()); //free ID
}
private Engine wireEngine(MainSettings mainSettings, MainAppData data, RobotKiller killer, RobotBreeder breeder, Population population)
{
EngineSettings engineSettings = getEngineSettings(mainSettings);
TimePointExecutor timePointExecutor = wireTimePointExecutor(mainSettings);
return(EngineFactory.getDefaultEngine(engineSettings, data, timePointExecutor, killer, breeder, population));
}
/// <summary>
/// Selects the krill with the best fitness function value
/// </summary>
public Krill GetBestKrill()
{
return(Population.Where(x => x.Fitness == MaximumFitness).First());
}
// Update is called once per frame
void FixedUpdate()
{
var gas = 1;
var steer = 1;
var moveDist = gas * Speed * Time.deltaTime;
var turnAngle = steer * TurnSpeed * Time.deltaTime * gas;
int i;
count++;
//StartCoroutine(MyMethod());
i = 0;
int j = 0;
//pop.incGenes();
foreach (GameObject x in carList)
{
if (x != null)
{
Car carCont = x.GetComponent <Car>();
if (carCont.roadNumber != 0)
{
j = pop.getChromozom()[i].getLastPos();
//Hareketleri yaptır.
//hareketleri yaptırırken
//Debug.Log("Chromosome:"+ i);
int temp = j;
while (j < pop.getChromozom()[i].getGenes().Count&& j - temp < Global.CHROMOSOME_SIZE)
{
//Debug.Log("gene:"+ pop.getChromozom()[i].getGenes()[j]);
if (pop.getChromozom()[i].getGenes()[j] == 0)
{
//float step = Speed * Time.deltaTime;
//x.transform.position += Vector3.MoveTowards(transform.position, x.transform.position, step);
x.transform.Translate(Vector3.forward * moveDist);
//Rigidbody rb = x.GetComponent<Rigidbody>();
//rb.MovePosition(Vector3.forward * moveDist);
// x.transform.position = x.transform.position + Vector3.forward * 1;
// x.transform.position += x.transform.forward * Time.deltaTime * 1;
}
else if (pop.getChromozom()[i].getGenes()[j] == 1)
{
x.transform.Rotate(new Vector3(0, 2, 0));
}
else if (pop.getChromozom()[i].getGenes()[j] == 2)
{
x.transform.Rotate(new Vector3(0, -2, 0));
}
j++;
}
if (j == pop.getChromozom()[i].getGenes().Count)
{
Debug.Log("Increasing...");
pop.incGenes();
}
pop.getChromozom()[i].setLastPos(j);
// if (pop.getChromozom()[i].getFitness() < carCont.roadNumber)
pop.getChromozom()[i].setFitness(carCont.roadNumber);
//Debug.Log("roadNum:" + pop.getChromozom()[i].getFitness());
}
}
i++;
}
int null_count = 0;
foreach (GameObject x in carList)
{
if (x == null)
{
null_count++;
}
}
if (null_count == carList.Count)
{
carList.Clear();
for (i = 0; i < Global.POPULATION_SIZE; i++)
{
GameObject obj = Instantiate(Car, Car.transform.position, Car.transform.rotation) as GameObject;
carList.Add(obj);
pop.getChromozom()[i].setLastPos(0);
}
Debug.Log("Evolving...");
Debug.Log("Genes: " + pop.getChromozom()[0].getGenes().Count);
pop.getChromozom().Sort((a, b) => a.getFitness().CompareTo(b.getFitness()));//fitnesa göre sort etmeyi ayarla
pop.getChromozom().Reverse();
//List<int> once = pop.getChromozom()[3].getGenes().ToList<int>();
pop = Global.generic.evolve(pop);
//List<int> sonra = pop.getChromozom()[3].getGenes().ToList<int>();
//Debug.Log("0 Degisti. : " + isEqual(once,sonra));
generation_number++;
Debug.Log("Generation: " + generation_number);
}
//carList.Clear();
//Debug.Log("---- Fitness ----");
//Debug.Log(pop.getChromozom()[0].getFitness());
//Debug.Log(pop.getChromozom()[0].getGenes()[0]);
//Debug.Log(pop.getChromozom()[1].getFitness());
//Debug.Log(pop.getChromozom()[2].getFitness());
//Debug.Log(pop.getChromozom()[3].getFitness());
//Debug.Log(pop.getChromozom()[4].getFitness());
//Debug.Log(pop.getChromozom()[5].getFitness());
//Debug.Log(pop.getChromozom()[6].getFitness());
//Debug.Log(pop.getChromozom()[7].getFitness());
//Debug.Log(pop.getChromozom()[8].getFitness());
//Debug.Log(pop.getChromozom()[9].getFitness());
//Debug.Log(pop.getChromozom()[0].getLastPos());
//Debug.Log(pop.getChromozom()[1].getLastPos());
//Debug.Log(pop.getChromozom()[2].getLastPos());
//Debug.Log(pop.getChromozom()[3].getLastPos());
//Debug.Log(pop.getChromozom()[4].getLastPos());
}
public static void Main(string[] args)
{
string plaintextFilename = "text_sample.txt";
string logFilename = "log.txt";
string key = "cipherkey";
int populationSize = 100;
int offspring = 10;
float mutationProbability = 0.25f;
float crossoverProbability = 0.85f;
int maxGenerations = 500;
using (StreamWriter w = File.AppendText($"../../../logs/{logFilename}"))
{
//read in plaintext
string plaintext = File.ReadAllText($"../../../statistics/text/{plaintextFilename}").ToLower();
//init cipher and encipher plaintext
ICipher cipher = new VigenereCipher(plaintext);
cipher.Encipher(key);
Console.WriteLine(cipher.CipherText);
//init genetic alorithm operators
//use tournament selection to minimize the likelyhood of staying in a lokal optima
var selection = new TournamentSelection(5);
//crossover the solution candidates by choosing a interval of the key and swap the interval
var crossover = new TwoPointCrossover();
//mutate the solution candidates with the own implemented mutation operator
var mutation = new CipherMutation();
//evaluate the fitness of the solution candidates with the own implemented fitness function
var fitness = new CipherFitness(cipher);
//initalize the solution candidates with the own implemented solution candidate implementation
var chromosome = new CipherChromosome(key.Length);
//initialize a population
var population = new Population(populationSize, populationSize + offspring, chromosome);
//initialize the genetic algorithm with the above initialized operators
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
//set mutation and crossover probabilities
ga.MutationProbability = mutationProbability;
ga.CrossoverProbability = crossoverProbability;
//specify the algorithm termination with max generations or a fitness threshold
ga.Termination = new OrTermination(new GenerationNumberTermination(maxGenerations), new FitnessThresholdTermination(0.99));
var latestFitness = 0.0;
//hang into the GenerationRan event which gets called by the library after each generation ran.
ga.GenerationRan += (sender, e) =>
{
//select the best solution candidate in the current generation
var bestChromosome = ga.BestChromosome as CipherChromosome;
//if fitness in current generation is better than fitness in previous generations
if (bestChromosome.Fitness != latestFitness)
{
latestFitness = bestChromosome.Fitness.Value;
string genText = String.Format("Generation {0}: Best solution found is Key:{1} with {2} fitness.", ga.GenerationsNumber, bestChromosome.ToString(), bestChromosome.Fitness);
Console.WriteLine(genText);
w.WriteLine(genText);
}
};
string gaText = $"------Plaintextfile:{plaintextFilename}, Key:{key}------\n------Genetic Algorithm Settings: Populationsize:{populationSize}, Mutation:{mutationProbability}, Crossover:{crossoverProbability}------";
Console.WriteLine(gaText);
w.WriteLine(gaText);
//start ga
ga.Start();
var finalSolution = ga.BestChromosome as CipherChromosome;
string doneText = $"GA done in {ga.GenerationsNumber} generations.\n Best solution found is Key:{finalSolution.ToString()} (L={finalSolution.ToString().Length}) with {finalSolution.Fitness} fitness. ";
string decipheredText = cipher.Decipher(finalSolution.ToString());
Console.WriteLine(doneText + "\n" + decipheredText);
w.WriteLine(doneText + "\n" + decipheredText);
}
}
public Population evolve(Population pop)
{
pop = mutate_population(crossover_population(pop));
//pop.getChromozom()[3].getGenes().Add(1);
return(pop);
}
public void NewGeneration()
{
if (Population.Count <= 0)
{
return;
}
CalculateFitness();
List <DNA <T> > newPopulation = new List <DNA <T> >();
Population.Sort
(
(DNA <T> a, DNA <T> b) =>
{
return(a.Fitness > b.Fitness ? -1 : 1);
}
);
if (Generation != 1)
{
string test = tests[method];
string baseDirUni = "M:\\aiproject\\";
string baseDirLaptop = "C:\\Users\\Sirius\\Desktop\\aiproject\\";
string baseDir = baseDirUni;
string printString;
string fitnessString;
string thrustString;
string judgementString;
string fuelString;
{
fitnessString = ((float)((int)(Population[0].Fitness * 100)) / 100).ToString();
thrustString = ((float)((int)(Population[0].Genes[0] * 100)) / 100).ToString();
judgementString = ((float)((int)(Population[0].Genes[1] * 100)) / 100).ToString();
fuelString = ((float)((int)(Population[0].Genes[2] * 100)) / 100).ToString();
printString = ("Worst: Fitness=" + fitnessString);
printString += (" Peak Thrust=" + thrustString);
printString += (" Judgement Height=" + judgementString);
printString += (" Starting Fuel=" + fuelString);
UnityEngine.Debug.Log(printString);
WriteToFile(fitnessString, thrustString, judgementString, fuelString, baseDir + test + "\\worst.csv");
}
{
float medianFitness = (Population[(Population.Count / 2) - 1].Fitness + Population[(Population.Count / 2) - 0].Fitness) / 2;
float medianGene0 = (Population[(Population.Count / 2) - 1].Genes[0] + Population[(Population.Count / 2) - 0].Genes[0]) / 2;
float medianGene1 = (Population[(Population.Count / 2) - 1].Genes[1] + Population[(Population.Count / 2) - 0].Genes[1]) / 2;
float medianGene2 = (Population[(Population.Count / 2) - 1].Genes[2] + Population[(Population.Count / 2) - 0].Genes[2]) / 2;
fitnessString = ((float)((int)(medianFitness * 100)) / 100).ToString();
thrustString = ((float)((int)(medianGene0 * 100)) / 100).ToString();
judgementString = ((float)((int)(medianGene1 * 100)) / 100).ToString();
fuelString = ((float)((int)(medianGene2 * 100)) / 100).ToString();
printString = ("Median: Fitness=" + fitnessString);
printString += (" Peak Thrust=" + thrustString);
printString += (" Judgement Height=" + judgementString);
printString += (" Starting Fuel=" + fuelString);
UnityEngine.Debug.Log(printString);
WriteToFile(fitnessString, thrustString, judgementString, fuelString, baseDir + test + "\\median.csv");
}
{
fitnessString = ((float)((int)(Population[Population.Count - 2].Fitness * 100)) / 100).ToString();
thrustString = ((float)((int)(Population[Population.Count - 2].Genes[0] * 100)) / 100).ToString();
judgementString = ((float)((int)(Population[Population.Count - 2].Genes[1] * 100)) / 100).ToString();
fuelString = ((float)((int)(Population[Population.Count - 2].Genes[2] * 100)) / 100).ToString();
printString = ("Second: Fitness=" + fitnessString);
printString += (" Peak Thrust=" + thrustString);
printString += (" Judgement Height=" + judgementString);
printString += (" Starting Fuel=" + fuelString);
UnityEngine.Debug.Log(printString);
WriteToFile(fitnessString, thrustString, judgementString, fuelString, baseDir + test + "\\second.csv");
}
{
fitnessString = ((float)((int)(Population[Population.Count - 1].Fitness * 100)) / 100).ToString();
thrustString = ((float)((int)(Population[Population.Count - 1].Genes[0] * 100)) / 100).ToString();
judgementString = ((float)((int)(Population[Population.Count - 1].Genes[1] * 100)) / 100).ToString();
fuelString = ((float)((int)(Population[Population.Count - 1].Genes[2] * 100)) / 100).ToString();
printString = ("First: Fitness=" + fitnessString);
printString += (" Peak Thrust=" + thrustString);
printString += (" Judgement Height=" + judgementString);
printString += (" Starting Fuel=" + fuelString);
UnityEngine.Debug.Log(printString);
WriteToFile(fitnessString, thrustString, judgementString, fuelString, baseDir + test + "\\first.csv");
}
}
for (int i = 0; i < Population.Count; i++)
{
//string printString;
//printString = ("Fitness=" + ((float)((int)(Population[i].Fitness * 100)) / 100).ToString());
//printString += (" Peak Thrust=" + ((float)((int)(Population[i].Genes[0] * 100)) / 100).ToString());
//printString += (" Judgement Height=" + ((float)((int)(Population[i].Genes[1] * 100)) / 100));
//printString += (" Starting Fuel=" + ((float)((int)(Population[i].Genes[2] * 100)) / 100));
//UnityEngine.Debug.Log(printString);
//DNA<T> parent1 = ChooseParent();
//DNA<T> parent2 = ChooseParent();
DNA <T> parent1;
DNA <T> parent2;
if (method == METHOD.TWOBEST)
{
parent1 = Population[Population.Count - 1];
parent2 = Population[Population.Count - 2];
}
else if (method == METHOD.ABOVEMEDIANONLY)
{
//int index = Population.Count - 1;
//index -= i / 2;
//parent1 = Population[index];
parent1 = ChooseParent(method);
parent2 = ChooseParent(method);
}
else if (method == METHOD.ROULETTEWHEEL)
{
parent1 = ChooseParent(method);
parent2 = ChooseParent(method);
}
else if (method == METHOD.ABOVEMEDIANSANDBEST)
{
parent1 = Population[Population.Count - 1];
parent2 = ChooseParent(method);
}
else
{
parent1 = Population[Population.Count - 1];
parent2 = Population[Population.Count - 2];
}
DNA <T> child = parent1.Crossover(parent2);
child.Mutate(MutationRate, MutationVariance);
newPopulation.Add(child);
}
Population = newPopulation;
Generation++;
}
public PieChartViewModel()
{
this.Expenditure = new List <CompanyExpense>();
Expenditure.Add(new CompanyExpense()
{
Expense = "Seeds", Amount = 7658d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Fertilizers", Amount = 7090d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Insurance", Amount = 3577d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Labor", Amount = 1473d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Warehousing", Amount = 820d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Taxes", Amount = 571d
});
Expenditure.Add(new CompanyExpense()
{
Expense = "Truck", Amount = 462d
});
CompanyDetails = new List <CompanyDetail>();
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Rolls Royce", CompanyTurnover = 750000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Benz", CompanyTurnover = 500000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Audi", CompanyTurnover = 450000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "BMW", CompanyTurnover = 700000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Mahindra", CompanyTurnover = 350000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Jaguar", CompanyTurnover = 650000
});
CompanyDetails.Add(new CompanyDetail()
{
CompanyName = "Hero Honda", CompanyTurnover = 250000
});
Metric = new List <Metrics>();
Metric.Add(new Metrics()
{
ResponseTime = 43, Utilization = 32
});
Metric.Add(new Metrics()
{
ResponseTime = 20, Utilization = 34
});
Metric.Add(new Metrics()
{
ResponseTime = 67, Utilization = 41
});
Metric.Add(new Metrics()
{
ResponseTime = 52, Utilization = 42
});
Metric.Add(new Metrics()
{
ResponseTime = 71, Utilization = 48
});
Metric.Add(new Metrics()
{
ResponseTime = 30, Utilization = 45
});
this.Population = new List <Populations>();
Population.Add(new Populations()
{
Continent = "Asia", Countries = "China", States = "Taiwan", PopulationinContinents = 50.02, PopulationinCountries = 26.02, PopulationinStates = 18.02
});
Population.Add(new Populations()
{
Continent = "Africa", Countries = "India", States = "Shandong", PopulationinContinents = 20.81, PopulationinCountries = 24, PopulationinStates = 8
});
Population.Add(new Populations()
{
Continent = "Europe", Countries = "Nigeria", States = "UP", PopulationinContinents = 15.37, PopulationinCountries = 12.81, PopulationinStates = 14.5
});
Population.Add(new Populations()
{
Countries = "Ethiopia", States = "Maharashtra", PopulationinCountries = 8, PopulationinStates = 9.5
});
Population.Add(new Populations()
{
Countries = "Germany", States = "Kano", PopulationinCountries = 8.37, PopulationinStates = 7.81
});
Population.Add(new Populations()
{
Countries = "Turkey", States = "Lagos", PopulationinCountries = 7, PopulationinStates = 5
});
Population.Add(new Populations()
{
States = "Oromia", PopulationinStates = 5
});
Population.Add(new Populations()
{
States = "Amhara", PopulationinStates = 3
});
Population.Add(new Populations()
{
States = "Hessen", PopulationinStates = 5.37
});
Population.Add(new Populations()
{
States = "Bayern", PopulationinStates = 3
});
Population.Add(new Populations()
{
States = "Istanbul", PopulationinStates = 4.5
});
Population.Add(new Populations()
{
States = "Ankara", PopulationinStates = 2.5
});
AdornmentsFac = new ResourceFactory();
AdornmentInfo = new ChartAdornmentInfo();
AdornmentInfo1 = new ChartAdornmentInfo();
AdornmentInfo2 = new ChartAdornmentInfo();
AdornmentInfo3 = new ChartAdornmentInfo();
AdornmentInfo4 = new ChartAdornmentInfo();
AdornmentInfo5 = new ChartAdornmentInfo();
AdornmentInfo6 = new ChartAdornmentInfo();
AdornmentInfo7 = new ChartAdornmentInfo();
AdornmentInfo8 = new ChartAdornmentInfo();
AdornmentInfo9 = new ChartAdornmentInfo();
AdornmentInfo10 = new ChartAdornmentInfo();
AdornmentInfo11 = new ChartAdornmentInfo();
AdornmentInfo.HorizontalAlignment = HorizontalAlignment.Center;
AdornmentInfo.VerticalAlignment = VerticalAlignment.Center;
AdornmentInfo.ShowConnectorLine = true;
AdornmentInfo.UseSeriesPalette = true;
AdornmentInfo.ConnectorHeight = 30;
AdornmentInfo.ShowLabel = true;
AdornmentInfo.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo.LabelTemplate = AdornmentsFac.labelTemplate8;
AdornmentInfo1.ShowLabel = true;
AdornmentInfo1.FontSize = 10;
AdornmentInfo1.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo1.LabelTemplate = AdornmentsFac.labelTemplate21;
AdornmentInfo2.ShowLabel = true;
AdornmentInfo2.FontSize = 10;
AdornmentInfo2.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo2.LabelTemplate = AdornmentsFac.labelTemplate22;
AdornmentInfo3.ShowLabel = true;
AdornmentInfo3.FontSize = 10;
AdornmentInfo3.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo3.LabelTemplate = AdornmentsFac.labelTemplate23;
AdornmentInfo4.ShowLabel = true;
AdornmentInfo4.SegmentLabelContent = LabelContent.Percentage;
AdornmentInfo4.SegmentLabelFormat = "##.#";
AdornmentInfo4.AdornmentsPosition = AdornmentsPosition.Bottom;
AdornmentInfo4.HorizontalAlignment = HorizontalAlignment.Center;
AdornmentInfo4.ConnectorHeight = 30;
AdornmentInfo4.VerticalAlignment = VerticalAlignment.Center;
AdornmentInfo4.ShowConnectorLine = true;
AdornmentInfo4.UseSeriesPalette = true;
AdornmentInfo5.ShowLabel = true;
AdornmentInfo5.UseSeriesPalette = true;
AdornmentInfo6.ShowLabel = true;
AdornmentInfo6.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo6.AdornmentsPosition = AdornmentsPosition.Bottom;
AdornmentInfo6.LabelTemplate = AdornmentsFac.labelTemplate21;
AdornmentInfo7.ShowLabel = true;
AdornmentInfo7.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo7.AdornmentsPosition = AdornmentsPosition.Bottom;
AdornmentInfo7.LabelTemplate = AdornmentsFac.labelTemplate22;
AdornmentInfo8.ShowLabel = true;
AdornmentInfo8.SegmentLabelContent = LabelContent.LabelContentPath;
AdornmentInfo8.AdornmentsPosition = AdornmentsPosition.Bottom;
AdornmentInfo8.LabelTemplate = AdornmentsFac.labelTemplate23;
AdornmentInfo9.ShowLabel = true;
AdornmentInfo9.ShowConnectorLine = true;
AdornmentInfo9.UseSeriesPalette = true;
AdornmentInfo9.ConnectorHeight = 17;
AdornmentInfo10.ShowLabel = true;
AdornmentInfo11.ShowLabel = true;
}
protected override IList <GeneticEntity> GetEliteGeneticEntitiesCore(Population population)
{
throw new Exception();
}
/// <summary>
/// Finishes a phase for the strategy.
/// </summary>
/// <param name="basePopulation">Population on which this phase was based.</param>
/// <returns>The <see cref="Population"/> for the next strategy to work with.</returns>
public Population FinishPhase(Population basePopulation)
{
return(this._population);
}
/// <inheritdoc />
public void Iteration()
{
if (!_initialized)
{
PreIteration();
}
if (Population.Species.Count == 0)
{
throw new EncogError("Population is empty, there are no species.");
}
IterationNumber++;
// Clear new population to just best genome.
_newPopulation.Clear();
_newPopulation.Add(BestGenome);
_oldBestGenome = BestGenome;
// execute species in parallel
IList <EAWorker> threadList = new List <EAWorker>();
foreach (ISpecies species in Population.Species)
{
int numToSpawn = species.OffspringCount;
// Add elite genomes directly
if (species.Members.Count > 5)
{
var idealEliteCount = (int)(species.Members.Count * EliteRate);
int eliteCount = Math.Min(numToSpawn, idealEliteCount);
for (int i = 0; i < eliteCount; i++)
{
IGenome eliteGenome = species.Members[i];
if (_oldBestGenome != eliteGenome)
{
numToSpawn--;
if (!AddChild(eliteGenome))
{
break;
}
}
}
}
// now add one task for each offspring that each species is allowed
while (numToSpawn-- > 0)
{
var worker = new EAWorker(this, species);
threadList.Add(worker);
}
}
// run all threads and wait for them to finish
Parallel.ForEach(threadList, currentTask => currentTask.PerformTask());
// validate, if requested
if (ValidationMode)
{
if (_oldBestGenome != null &&
!_newPopulation.Contains(_oldBestGenome))
{
throw new EncogError(
"The top genome died, this should never happen!!");
}
if (BestGenome != null &&
_oldBestGenome != null &&
BestComparer.IsBetterThan(_oldBestGenome,
BestGenome))
{
throw new EncogError(
"The best genome's score got worse, this should never happen!! Went from "
+ _oldBestGenome.Score + " to "
+ BestGenome.Score);
}
}
_speciation.PerformSpeciation(_newPopulation);
// purge invalid genomes
Population.PurgeInvalidGenomes();
PostIteration();
}
/// <summary>
/// Calculates/generates a next generation based on the given one.
/// </summary>
/// <param name="population"></param>
/// <returns></returns>
private Population <GenomeType, ProblemType, WeightType> NextGeneration(Population <GenomeType, ProblemType, WeightType> population)
{
Population <GenomeType, ProblemType, WeightType> next_population =
new Population <GenomeType, ProblemType, WeightType>(true);
// do elitims selection.
int elitism_count = (int)(population.Count * (_settings.ElitismPercentage / 100f));
next_population.AddRange(
population.GetRange(
0,
elitism_count));
// do selection/cross-over.
//Tools.Core.Output.OutputTextStreamHost.Write(" C:");
int cross_over_count = (int)(population.Count *
(_settings.CrossOverPercentage / 100f)) + elitism_count;
while (next_population.Count < cross_over_count &&
next_population.Count < population.Count)
{
// select two individuals.
Individual <GenomeType, ProblemType, WeightType> individual1 = _selector.Select(
this,
population,
null);
Individual <GenomeType, ProblemType, WeightType> individual2 = _selector.Select(
this,
population,
new HashSet <Individual <GenomeType, ProblemType, WeightType> >(
new Individual <GenomeType, ProblemType, WeightType>[] { individual1 }));
// cross-over.
Individual <GenomeType, ProblemType, WeightType> new_individual =
_cross_over_operation.CrossOver(
this,
individual1,
individual2);
new_individual.CalculateFitness(_problem, _fitness_calculator);
if (_accept_only_better_on_cross_over)
{
if (new_individual.Fitness.CompareTo(individual1.Fitness) < 0 &&
new_individual.Fitness.CompareTo(individual2.Fitness) < 0)
{
// add to the new population.
next_population.Add(new_individual);
}
else
{
//next_population.Add(individual1);
if (individual2.Fitness.CompareTo(individual1.Fitness) < 0)
{
// add to the new population.
next_population.Add(individual2);
}
else
{
next_population.Add(individual1);
}
}
}
else
{
// add to the new population.
next_population.Add(new_individual);
}
//this.ReportNew("Crossing over population!", next_population.Count, population.Count);
//Tools.Core.Output.OutputTextStreamHost.Write(".");
}
// do mutation to generate the rest of the population.
int mutation_count =
(int)(population.Count * (_settings.MutationPercentage / 100f)) + cross_over_count;
int mutation_count_max_tries = mutation_count * 2; // try mutation 10 times more.
int mutation_idx_absolute = 0;
//Tools.Core.Output.OutputTextStreamHost.Write(" m:");
while (next_population.Count < mutation_count &&
next_population.Count < population.Count)
{
// get random individual.
int individual_to_mutate_idx = this.Random.Next(population.Count);
Individual <GenomeType, ProblemType, WeightType> individual_to_mutate =
population[individual_to_mutate_idx];
// mutate.
Individual <GenomeType, ProblemType, WeightType> mutation = _mutation_operation.Mutate(
this,
individual_to_mutate);
//if (!mutation.Equals(individual_to_mutate))
//{ // do not add indentical ones.
// add to population.
if (_accept_only_better_on_mutation)
{
mutation.CalculateFitness(_problem, _fitness_calculator);
if (mutation.Fitness.CompareTo(individual_to_mutate.Fitness) < 0)
{
next_population.Add(mutation);
}
}
else
{
next_population.Add(mutation);
}
// //Tools.Core.Output.OutputTextStreamHost.Write(".");
// this.ReportNew("Mutating population!", next_population.Count, population.Count);
//}
// increase the absolute count.
mutation_idx_absolute++;
if (mutation_idx_absolute > mutation_count_max_tries)
{ // stop trying to mutate not new individuals could be found.
break;
}
}
while (next_population.Count < population.Count)
{
// get random individual.
int individual_to_mutate_idx = this.Random.Next(population.Count);
Individual <GenomeType, ProblemType, WeightType> individual =
population[individual_to_mutate_idx];
// place in the new population.
next_population.Add(individual);
this.ReportNew("Filling population!", next_population.Count, population.Count);
}
return(next_population);
}
public void ExtractIndividualsFrom(Population population, params int[] individualIds)
{
ExtractIndividualsFrom(population, individualIds.ToList());
}
public void Solve(Maze maze)
{
const int NumberOfGenes = 5000;
const float MutationRate = 0.02f;
const float CrossOverRate = 0.6f;
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new MazeSolverChromosome(NumberOfGenes);
var reinsertion = new ElitistReinsertion();
var population = new Population(50, 100, chromosome);
var fitness = new MazeWalkerFitness(maze);
IChromosome best = chromosome;
best.Fitness = fitness.Evaluate(best);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = MutationRate;
ga.Reinsertion = reinsertion;
ga.Termination = new OrTermination(
new FitnessStagnationTermination(this.stagnationThreshold),
new TimeEvolvingTermination(TimeSpan.FromSeconds(this.maxRunTimeInSeconds)));
ga.CrossoverProbability = CrossOverRate;
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = Environment.ProcessorCount,
MaxThreads = Environment.ProcessorCount
};
ga.GenerationRan += (sender, eventargs) =>
{
if (this.generateUpdateImages)
{
if (ga.GenerationsNumber == 1 || ga.GenerationsNumber % this.updateImageFrequency == 0)
{
var winnerSteps = FillMazeStepsWalked(maze, ga.BestChromosome);
ImageDraw(winnerSteps, $"gen{ga.GenerationsNumber}.png");
}
}
if (ga.GenerationsNumber % 10 == 0)
{
Console.WriteLine(
$"{ga.GenerationsNumber} generations completed. Best fitness: {ga.BestChromosome.Fitness}. Best so far: {best.Fitness}. Time evolving: {ga.TimeEvolving.TotalMinutes} min.");
}
if (ga.BestChromosome.Fitness > best.Fitness)
{
best = ga.BestChromosome.Clone();
// ga.Population.CurrentGeneration.Chromosomes.Add(best);
}
};
ga.TerminationReached += (sender, eventargs) =>
{
Console.WriteLine($"Termination Reached");
var winnerSteps = FillMazeStepsWalked(maze, best);
ImageDraw(winnerSteps, "best.png");
var serializer = new XmlSerializer(typeof(string[]));
using (var sw = new StreamWriter("bestchromosome.xml"))
{
serializer.Serialize(sw, best.GetGenes().Select(g => g.Value as string).ToArray());
}
if (this.animate)
{
File.Delete("output.gif");
this.gif.Save("output.gif");
}
};
if (this.generateUpdateImages)
{
this.ImageDraw(maze, "gen0.png");
}
ga.Start();
}
private void LoadAllPeople(Population pop)
{
float errorCounter = 0;
float totalError = 0;
bool recklessPlacement = false;
for (int i = 0; i < pop.currentPopulation; ++i)
{
Vector2 creationSpot = new Vector2(Random.Range(-Globals.mapRadiusX, Globals.mapRadiusX), Random.Range(-Globals.mapRadiusY, Globals.mapRadiusY));
RaycastHit2D[] allHit = Physics2D.RaycastAll(creationSpot, -Vector2.up,0);
if (allHit.Length == 0 || recklessPlacement)
{
GameObject go = Instantiate(Resources.Load("Villager"), creationSpot, Quaternion.identity) as GameObject;
go.GetComponent<Villager>().Initialize(pop.averagePercentLifePoints, pop.averageHappiness, pop.averageHealthiness);
} else {
errorCounter++;
totalError++;
if (errorCounter > 10) //too much wait, that vilagers toast, next one
{
errorCounter = 0;
++i;
}
if (totalError > 40) //too much thrashing, just place them ontop other objects and hope unity forces them out
{
recklessPlacement = true;
}
}
}
}
