// start with the selected settings
public void RunSettings()
{
if (ReadFromFileField.isOn && !File.Exists(MapFilePathField.text))
{
ColorBlock cb = MapFilePathField.colors;
cb.normalColor = new Color(1f, 0.7f, 0.7f);
MapFilePathField.colors = cb;
return;
}
ReadMapFromFile = ReadFromFileField.isOn;
MapFilePath = MapFilePathField.text;
MapWidth = int.Parse(MapWidthField.text);
MapHeight = int.Parse(MapHeightField.text);
NumberOfGuards = int.Parse(NumberOfGuardsField.text);
RouteGenotypeLength = int.Parse(GenotypeLengthField.text);
CrossoverPerStage = int.Parse(CrossoverNumberField.text);
DetectionRadius = int.Parse(DetectionRadiusField.text);
DetectionDecayTime = int.Parse(DetectionDecayField.text);
FitnessFunction = (FitnessFunctions)(FitnessFunctionField.value);
NumberOfSchemes = int.Parse(NumberOfSchemesField.text);
SimpleCrossover = SimpleCrossoverField.isOn;
UseMinFunction = UseMinFunctionField.isOn;
MutationProbability = float.Parse(MutationProbabilityField.text);
RouteMutationProbability = float.Parse(RouteMutationProbabilityField.text);
NumberOfIterations = int.Parse(NumberOfIterationsField.text);
FitnessBinsX = int.Parse(FitnessBinsXField.text);
FitnessBinsY = int.Parse(FitnessBinsYField.text);
RandomSeed = int.Parse(RandomSeedField.text);
TurnTime = float.Parse(TurnTimeField.text);
TurnLimit = int.Parse(TurnLimitField.text);
EnvironmentalNoiseProbability = float.Parse(EnvNoiseProbabilityField.text);
EnvironmentalNoiseDegree = int.Parse(EnvNoiseDegreeField.text);
CrawlNoise = float.Parse(CrawlNoiseField.text);
WalkNoise = float.Parse(WalkNoiseField.text);
RunNoise = float.Parse(RunNoiseField.text);
NoiseRandomRange = float.Parse(NoiseRadiusField.text);
MinOutputToCheck = float.Parse(MinOutputCheckField.text);
MinOutputForAlarm = float.Parse(MinOutputAlarmField.text);
RandomNinjaNumber = int.Parse(RandomNinjaNumberField.text);
SettingsCanvas.gameObject.SetActive(false);
RouteCanvas.gameObject.SetActive(true);
if (RandomSeed != -1)
{
Random.InitState(RandomSeed);
}
if (!GenerateMap())
{
return;
}
SetGuards();
GameStage = GameStages.RouteGeneration;
}
public Population(int size, Chromosomes.IChromosomeFactory chromosomeFactory, FitnessFunctions.IFitnessFunction fitnessFunction, SelectionMethods.ISelectionMethod selectionMethod)
{
this.SetCrossoverProbabilityToDefault();
this.SetMutationProbabilityToDefault();
this.SetSelectionRateToDefault();
this.size = size;
this.individuals = new Chromosomes.IChromosome[size];
for (int i = 0; i < size; ++i)
this.individuals[i] = chromosomeFactory.GetNewChromosome();
this.fitnessCalculator = fitnessFunction;
this.selector = selectionMethod;
this.fitnesses = new double[this.size];
for (int i = 0; i < this.size; ++i)
this.fitnesses[i] = this.fitnessCalculator.GetFitness(this.individuals[i]);
}
public static void RunWickedCombOptimization()
{
var solutionSpace = new IntervalBranch(-0.15, 0.15); // Constraints
const double difficulty = 5;
const double xSquaredCoefficient = 10;
const int sampleSize = 200;
const double confidenceLevel = 0.99;
const int numSteps = 15;
var bb = new SIDBranchAndBound <double>(solutionSpace, x => FitnessFunctions.WickedComb(x, difficulty, xSquaredCoefficient), GuidingParameter.OneOverNthQuantile);
var result = bb.BranchAndBound(sampleSize, numSteps, confidenceLevel, false, false);
Program.logger.WriteLine($"Wicked Comb (x, {difficulty}, {xSquaredCoefficient}) Size {sampleSize} Confidence {confidenceLevel} Optimization Results:");
for (int i = 0; i < result.Length; i++)
{
Program.logger.WriteLine($"{i}: {result[i].ToString()}");
}
}
public static void RunEggholderOptimization()
{
var solutionSpace = new RectangleBranch(-512, 512, -512, 512); // Constraints
const int sampleSize = 200;
const double confidenceLevel = 0.99;
const int numSteps = 6;
var bb = new SIDBranchAndBound <Tuple <double, double> >(solutionSpace, x => FitnessFunctions.EggHolder(x.Item1, x.Item2), GuidingParameter.OneOverNthQuantile);
var result = bb.BranchAndBound(sampleSize, numSteps, confidenceLevel, false, false);
Program.logger.WriteLine($"Eggholder Size {sampleSize} Confidence {confidenceLevel} Optimization Results:");
for (int i = 0; i < result.Length; i++)
{
Program.logger.WriteLine($"{i}: {result[i].ToString()}");
}
}
