public FitnessFunctions()
{
this.mode = FitnessMode.MRE; // ToDo à modifier depuis l'interface utilisateur
this.ParamValueDictio = new Dictionary <string, Dictionary <string, double> >();
paramValueDictioSize = 0;
this.ParamSubModel = new Dictionary <string, string>();
this.ObservationsValue = new Dictionary <RunInfo, List <KeyValuePair <OptiObservation, Tuple <DateTime, double> > > >();
optimizeManagementParameter = false;
optimizeSoilParameter = false;
optimizeVarietalParameter = false;
optimizeNonVarietalParameter = false;
// Soil Parameters
this.ParamSubModel.Add("Kq", "Soil");
this.ParamSubModel.Add("Ko", "Soil");
this.ParamSubModel.Add("No", "Soil");
this.ParamSubModel.Add("MinNir", "Soil");
this.ParamSubModel.Add("Ndp", "Soil");
// Management Parameters
this.ParamSubModel.Add("SowingDate", "Management");
this.ParamSubModel.Add("TotalNi", "Management");
this.ParamSubModel.Add("CO2", "Management");
this.ParamSubModel.Add("SowingDensity", "Management");
this.ParamSubModel.Add("TopNi", "Management");
this.ParamSubModel.Add("MidNi", "Management");
this.ParamSubModel.Add("SoilWaterDeficit", "Management");
this.ParamSubModel.Add("TargetFertileShootNumber", "Management");
run = new Run();
this.RunInfos = new List <RunInfo>();
this.Managements = new HashSet <ManagementItem>();
this.RunOptions = new HashSet <RunOptionItem>();
this.Sites = new HashSet <SiteItem>();
this.Soils = new HashSet <SoilItem>();
this.Varieties = new HashSet <CropParameterItem>();
this.NonVarieties = new HashSet <CropParameterItem>();
multiMultiYear = false;
multiFirstYear = 0;
multiLastYear = 0;
}
/// <summary>
/// Applies Roulette Wheel sampling to the chromosomes.
/// </summary>
/// <param name="pool">Pool of chromosomes to select from.</param>
/// <returns></returns>
public IEnumerable <IChromosome> Select(IEnumerable <IChromosome> pool, FitnessMode fitnessMode)
{
Vector weights = pool.Select(s => s.Weight).ToVector();
return(this._Filter.Filter(pool, weights));
}
/// <summary>
/// Initializes the genetic pool using the specified seed and mutation functions.
/// </summary>
/// <param name="poolSize">Size of the pool to create.</param>
/// <param name="seed">Starting seed value.</param>
/// <param name="seedMutator">Mutator used during initialization of the pool.</param>
/// <param name="fitnessMetric">Fitness function for evaluating chromosomes.</param>
/// <param name="fitnessMode">Fitness mode.</param>
public Population(int poolSize, IChromosome seed, IMutationFunction seedMutator, IFitnessMetric fitnessMetric, FitnessMode fitnessMode)
: this(poolSize, () => seedMutator.Mutate(seed), fitnessMetric, fitnessMode)
{
}
/// <summary>
/// Initializes a new Population using a factory and the specified fitness.
/// </summary>
/// <param name="pool"></param>
/// <param name="poolSize">Size of the pool to create.</param>
/// <param name="fnInitializer">Initialization function to be called for each new chromosome.</param>
/// <param name="fitnessMetric">Fitness function for evaluating chromosomes.</param>
/// <param name="fitnessMode">Fitness mode.</param>
public Population(int poolSize, Func <IChromosome> fnInitializer, IFitnessMetric fitnessMetric, FitnessMode fitnessMode)
: this(fitnessMetric, fitnessMode)
{
var chromosomes = new IChromosome[poolSize];
// init pool
for (int i = 0; i < poolSize; i++)
{
var clone = fnInitializer();
clone.Fitness = this.FitnessMetric.Fitness(clone);
clone.Generation = 0;
chromosomes[i] = clone;
}
this.Apply(chromosomes);
}
/// <summary>
/// Initializes a new Population using existing chromosomes.
/// </summary>
/// <param name="chromosomes">Chromosomes to use in the population.</param>
/// <param name="fitnessMetric">Fitness function for evaluating chromosomes.</param>
/// <param name="fitnessMode">Fitness mode.</param>
public Population(IEnumerable <IChromosome> chromosomes, IFitnessMetric fitnessMetric, FitnessMode fitnessMode)
: this(fitnessMetric, fitnessMode)
{
this.Apply(chromosomes);
}
protected Population(IFitnessMetric fitnessMetric, FitnessMode fitnessMode)
{
this.FitnessMetric = fitnessMetric;
this.FitnessMode = fitnessMode;
}
/// <summary>
/// Evolves using a standard genetic algorithm.
/// </summary>
/// <param name="chromosomes">Chromosomes to undergo evolution.</param>
/// <param name="fitnessMetric">Fitness metric to use for evaluation.</param>
/// <param name="fitnessMode">Fitness mode for evaluating performance.</param>
public virtual IEnumerable <IChromosome> Evolve(IEnumerable <IChromosome> chromosomes, IFitnessMetric fitnessMetric, FitnessMode fitnessMode)
{
int count = chromosomes.Count();
var candidates = new List <IChromosome>();
int elites = (int)System.Math.Ceiling(chromosomes.Count() * this.ElitistRate);
var topCandidates = (chromosomes.OrderByDescending(o => o.Weight).Take(elites));
foreach (var pair in this.Pairing.Pair(chromosomes))
{
(IChromosome c1, IChromosome c2) = pair;
IChromosome candidate = null;
double rand = Sampling.GetUniform();
if (rand < this.CrossoverRate)
{
candidate = this.Crossover.Crossover(c1, c2);
}
double selector = Sampling.GetUniform();
candidate = candidate ?? (selector <= 0.5 ? c1 : c2);
if (rand < this.MutationRate)
{
candidate = this.Mutation.Mutate(candidate.Clone());
}
candidates.Add(candidate);
}
foreach (var elite in topCandidates)
{
candidates.Add(elite);
}
var result = this.Selection.Select(candidates, fitnessMode).Take(count);
return(result);
}
public FitnessCalculator(Airfoil.AirfoilManager airfoil, FitnessMode fitnessMode)
{
this.airfoil = airfoil;
this.fitnessMode = fitnessMode;
}
