public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.Error.WriteLine("You have to at least specify the data file path");
return;
}
var mutation = new UniformMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new ArithmeticCrossover();
var fitnessFunction = new FitnessFunction(args[0]);
IGeneticAlgorithm <DecimalArrayChromosome> geneticAlgorithm;
if (args.Length == 2 && args[1].ToLower() == "gen")
{
geneticAlgorithm = new GenerationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
else
{
geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
var optimum = geneticAlgorithm.FindOptimum();
Console.WriteLine();
Console.WriteLine(optimum);
}
public static void Main(string[] args)
{
var dataset = new Dataset.Dataset(DatasetFilePath);
var ann = new ANN(Architecture);
var fitness = new FitnessFunction(ann, dataset);
var mutation = new GaussMutation(MutationThreshold, MutationProbability, Sigma1, Sigma2);
var selection = new KTournamentSelection <DoubleArrayChromosome>(TournamentSize);
var crossovers = new List <ICrossover <DoubleArrayChromosome> >()
{
new ArithmeticCrossover(),
new HeuristicCrossover(),
new UniformCrossover()
};
var geneticAlgorithm =
new EliminationGeneticAlgorithm(mutation, selection, crossovers, fitness, IterationLimit, ErrorLimit, PopulationSize);
var optimum = geneticAlgorithm.FindOptimum();
var correctClassification = 0;
foreach (var sample in dataset)
{
var classification = ann.CalculateOutput(sample.Input, optimum.Values);
var correct = true;
for (int i = 0; i < classification.Length; i++)
{
classification[i] = classification[i] < 0.5 ? 0 : 1;
if (Math.Abs(classification[i] - sample.Classification[i]) > 10e-9)
{
correct = false;
}
}
Console.WriteLine(classification[0] + " " + classification[1] + " " + classification[2] +
" <=> " + sample.Classification[0] + " " + sample.Classification[1] + " " + sample.Classification[2] + " ");
if (correct)
{
correctClassification++;
}
}
Console.WriteLine("Correct => " + correctClassification + ", Total => " + dataset.Count());
ann.WriteNeuronLayerParametersToFile(ParametersFilePath, 1, optimum.Values);
}
public static void Main(string[] args)
{
FitnessFunction.Rides = ParseData(out int fleetSize, out int bonus, out int steps);
Chromosome.FleetSize = fleetSize;
FitnessFunction.NumberOfSteps = steps;
FitnessFunction.Bonus = bonus;
var mutation = new UniformMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new UniformCrossover();
var fitnessFunction = new FitnessFunction();
var geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize, FitnessFunction.Rides.Count);
var optimum = geneticAlgorithm.FindOptimum();
WriteOutput(optimum);
}
public static void Main(string[] args)
{
var busStops = new List <BusStop>();
var students = new List <Student>();
ParseFromFile(busStops, students);
var mutation = new StudentBusMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new StudentBusCrossover();
var fitnessFunction = new FitnessFunction.FitnessFunction();
var geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize, students, busStops, InstanceFilePath);
var optimum = geneticAlgorithm.FindOptimum();
Console.WriteLine();
Console.WriteLine(optimum.Fitness);
optimum.Students = optimum.Students.OrderBy(x => x.Id).ToList();
HelperMethods.WriteToFile(optimum.ToString(), InstanceResultFilePath);
}
