/// <summary>
/// Selects all chromosomes with positive fitness from given population
/// </summary>
/// <param name="population">Population</param>
/// <returns></returns>
public static IList <IChromosome> SelectProfitableChromosomes(this PopulationBase population)
{
var completeList = new List <IChromosome>();
foreach (var g in population.Generations)
{
completeList.AddRange(g.Chromosomes);
}
return(completeList.SelectDistinct()
.Where(c => c.Fitness != null && c.Fitness.Value > 0)
.OrderByDescending(c => c.Fitness.Value)
.ToList());
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticAlgorithm"/> class.
/// </summary>
/// <param name="population">The chromosomes population.</param>
/// <param name="fitness">The fitness evaluation function.</param>
/// <param name="taskExecutor">Task executor.</param>
public GeneticAlgorithm(
PopulationBase population,
IFitness fitness,
ITaskExecutor taskExecutor)
{
Population = population;
Fitness = fitness;
TaskExecutor = taskExecutor;
// Initial state values ->
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
// Init mutation - Will replace a gene at random position with a new randomly generated gene ->
Mutation = new UniformMutation();
// Collection of crossover operators to use ->
CrossoverCollection = new List <ICrossover>();
}