public static async Task Main(string[] args)
{
Console.WriteLine($"[{DateTime.Now}] Tuner start");
SettingsLoader.Init("settings.json");
_webService = new WebService();
_generationStopwatch = new Stopwatch();
await _webService.EnableIfAvailable();
_testId = await _webService.RegisterTest();
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(true);
var fitness = new EvaluationFitness(_testId, _webService);
var chromosome = new EvaluationChromosome();
var population = new Population(SettingsLoader.Data.MinPopulation, SettingsLoader.Data.MaxPopulation, chromosome);
var geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
geneticAlgorithm.Termination = new GenerationNumberTermination(SettingsLoader.Data.GenerationsCount);
geneticAlgorithm.GenerationRan += GeneticAlgorithm_GenerationRan;
_generationStopwatch.Start();
geneticAlgorithm.Start();
Console.WriteLine("Best solution found has {0} fitness.", geneticAlgorithm.BestChromosome.Fitness);
Console.ReadLine();
}
static async Task Main(string[] args)
{
Console.WriteLine($"[{DateTime.Now}] Tuner start");
SettingsLoader.Init("settings.json");
IFitness fitness = null;
IChromosome chromosome = null;
_webService = new WebService();
await _webService.EnableIfAvailable();
_testId = await _webService.RegisterTest(new RegisterTestRequest
{
Type = TestType.Texel
});
var scalingFactorMode = args.Contains("scaling_factor");
if (scalingFactorMode)
{
SettingsLoader.Data.Genes.Clear();
SettingsLoader.Data.Genes.Add(new GeneInfo
{
Name = "ScalingFactor",
MinValue = 0,
MaxValue = 2000
});
SettingsLoader.Data.Genes.Add(new GeneInfo
{
Name = "Unused",
MinValue = 0,
MaxValue = 1
});
fitness = new ScalingFactorFitness(_testId, _webService);
chromosome = new ScalingFactorChromosome();
}
else
{
fitness = new EvaluationFitness(_testId, _webService);
chromosome = new EvaluationChromosome();
}
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(true);
var population = new Population(SettingsLoader.Data.MinPopulation, SettingsLoader.Data.MaxPopulation, chromosome);
var geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
geneticAlgorithm.Termination = new GenerationNumberTermination(SettingsLoader.Data.GenerationsCount);
geneticAlgorithm.GenerationRan += GeneticAlgorithm_GenerationRan;
_generationStopwatch = new Stopwatch();
_generationStopwatch.Start();
geneticAlgorithm.Start();
Console.WriteLine("Best solution found has {0} fitness.", geneticAlgorithm.BestChromosome.Fitness);
Console.ReadLine();
}
static void Main(string[] args)
{
SettingsLoader.Init("settings.json");
new TournamentArbiter().Run();
}
