/// <summary>
/// We compare to see if it is worth changing the position of the food
/// </summary>
private Vector <double> CompareToFoodLastPosition(Vector <double> centerOfFoodCoordinates)
{
if (FoodLastPosition != null)
{
if (FitnessFunction.Invoke(FoodLastPosition.ToArray()) > FitnessFunction.Invoke(centerOfFoodCoordinates.ToArray()))
{
centerOfFoodCoordinates = FoodLastPosition;
}
}
FoodLastPosition = centerOfFoodCoordinates;
return(centerOfFoodCoordinates);
}
/// <summary>
/// Evolve the the population to the next generation.
/// </summary>
public void Evolve()
{
foreach (T chromosome in _population)
{
chromosome.FitnessValue = _fitnessFunction.Invoke(chromosome);
}
GetFittestChromsome();
IList <T> selection = _selection.ApplySelection(_population).Select(x => x.Clone()).ToArray();
foreach (IEvolveFunction function in _eveolveFunctions)
{
function.Execute(selection, this._population);
}
EnsureSurvival();
foreach (T chromosome in _population)
{
chromosome.OnEvolved();
}
this.CurrentGeneration++;
}
/// <summary>
/// Calculation of fitness for a given krill
/// </summary>
public double Evaluate(FitnessFunction fitnessFunction)
{
Fitness = fitnessFunction.Invoke(Coordinates.ToArray());
if (Fitness > BestFitness)
{
BestFitness = Fitness;
BestCoordinates = Coordinates;
}
return(Fitness);
}
/// <summary>
/// Evaluates the Chromosome by invoking the specified delegate method.
/// The fitness function should return a higher
/// value for those chromosomes that are deemed fitter.
/// </summary>
/// <param name="fitnessFunctionDelegate"></param>
/// <returns></returns>
public double Evaluate(FitnessFunction fitnessFunctionDelegate)
{
if (fitnessFunctionDelegate == null)
{
throw new ArgumentNullException("nameof (fitnessFunctionDelegate)");
}
var fitness = fitnessFunctionDelegate.Invoke(this);
if (fitness < 0 || fitness > 1.0)
{
throw new EvaluationException("The fitness value must be within the range 0.0 to 1.0.");
}
Fitness = fitness;
return(fitness);
}
/// <summary>
/// EQUATION 13
/// </summary>
private Vector <double> Beta_i_food(Krill krill, int currentIteration, Vector <double> vf_position)
{
Vector <double> result = Vector <double> .Build.Dense(krill.Coordinates.Count);
var K_food = FitnessFunction.Invoke(vf_position.ToArray());
if (K_food > krill.Fitness)
{
double C_food = EffectiveFoodCoefficient(currentIteration);
double K_i_food = K_i_j(krill.Fitness, K_food);
Vector <double> X_i_food = X_i_j(krill.Coordinates, vf_position);
result = X_i_food.Multiply(K_i_food * C_food);
}
return(result);
}
public double Evaluate(FitnessFunction fitnessFunction)
{
Fitness = fitnessFunction.Invoke(this, Dimensions);
return(Fitness);
}
