private void InitializePopulation()
{
for (int i = 0; i < PopulationSize; i++)
{
var chromosome = new DoubleArrayChromosome(FitnessFunction.GetNumberOfParameters(), randomize: true);
chromosome.Fitness = FitnessFunction.CalculateFitness(chromosome);
Population.AddChromosome(chromosome);
}
}
public override DoubleArrayChromosome FindOptimum()
{
var best = new DoubleArrayChromosome(FitnessFunction.GetNumberOfParameters())
{
Fitness = double.MaxValue
};
for (var i = 0; i < IterationLimit; i++)
{
var selectedFromPopulation = Selection.Select(Population).OrderBy(x => x.Fitness).ToList();
var crossoverIndex = HelperMethods.Random.Next(Crossovers.Count);
var childChromosome = Crossovers[crossoverIndex].Cross(selectedFromPopulation[0], selectedFromPopulation[1]);
childChromosome = Mutation.Mutate(childChromosome);
childChromosome.Fitness = FitnessFunction.CalculateFitness(childChromosome);
Population.RemoveChromosome(selectedFromPopulation[2]);
Population.AddChromosome(childChromosome);
var populationBest = double.MaxValue;
var bestIndex = 0;
var index = 0;
foreach (var chromosome in Population)
{
if (chromosome.Fitness < populationBest)
{
populationBest = chromosome.Fitness;
bestIndex = index;
}
index++;
}
if (populationBest < best.Fitness)
{
best = Population[bestIndex];
Console.WriteLine("Iteration: " + i + ", Best fitness: " + populationBest);
}
if (populationBest < FitnessTerminator)
{
break;
}
}
return(best);
}