public void Start()
{
GeneFactory.Initialize(_config.Genes);
var chromosome = new Chromosome(false, GeneFactory.Config);
//todo: should use return value
var cost = _fitness.Evaluate(chromosome);
LogProvider.GenerationsLogger.Info(Termination);
var best = (Chromosome)((SharpeMaximizer)_fitness).Best;
var info = $"Algorithm: {_config.AlgorithmTypeName}, Fitness: {chromosome.Fitness}, {_fitness.Name}: " +
$"{cost.ToString("F")}, {best.ToKeyValueString()}";
LogProvider.GenerationsLogger.Info(info);
}
public void Start()
{
if (_executor == null)
{
throw new Exception("Executor was not initialized");
}
//create the population
IList <IChromosome> list = new List <IChromosome>();
GeneFactory.Initialize(_config.Genes);
for (int i = 0; i < _config.PopulationSize; i++)
{
//first chromosome always use actuals. For others decide by config
var isActual = i == 0 || _config.UseActualGenesForWholeGeneration;
list.Add(new Chromosome(isActual, GeneFactory.Config));
}
int max = _config.PopulationSizeMaximum < _config.PopulationSize ? _config.PopulationSize * 2 : _config.PopulationSizeMaximum;
_population = new PreloadPopulation(_config.PopulationSize, max, list);
_population.GenerationStrategy = new PerformanceGenerationStrategy();
//create the GA itself
var ga = new GeneticAlgorithm(_population, _fitness, new TournamentSelection(),
_config.OnePointCrossover ? new OnePointCrossover() : new TwoPointCrossover(), new UniformMutation(true));
//subscribe to events
ga.GenerationRan += GenerationRan;
ga.TerminationReached += TerminationReached;
ga.TaskExecutor = _executor;
ga.Termination = new OrTermination(new FitnessStagnationTermination(_config.StagnationGenerations), new GenerationNumberTermination(_config.Generations));
ga.Reinsertion = new ElitistReinsertion();
ga.MutationProbability = _config.MutationProbability;
ga.CrossoverProbability = _config.CrossoverProbability;
//run the GA
ga.Start();
}