public override Genome Start()
{
int i = 0;
Genome lastBest = new Genome(null);
RandomPopulation(Functions.ParamSize);
Console.Write(i + " iteration. Current best: ");
Program.PrintParameters(BestGenome.Genes);
Console.WriteLine("with fitness: " + BestGenome.Fitness.ToString("G10"));
while (BestGenome.Fitness > _data.MinError && ++i < _data.MaxIterations)
{
lastBest.Copy(BestGenome);
if (_data.Elitism)
{
_tempPopulation[0].Copy(BestGenome);
}
double unused = CalculateFitness();
Parallel.For(_data.Elitism ? 1 : 0, _data.PopulationSize, SingleThread);
SwapBuffers();
DeterminePopulationFitness();
if (!(BestGenome.Fitness < lastBest.Fitness))
{
continue;
}
Console.Write(i + " iteration. Current best: ");
Program.PrintParameters(BestGenome.Genes);
Console.WriteLine("with fitness: " + BestGenome.Fitness.ToString("G10"));
}
return(BestGenome);
}
public override Genome Start()
{
int i = 0;
int howManyDies = (int)(_data.Mortality * _data.PopulationSize);
Genome lastBest = new Genome(null);
RandomPopulation(Functions.ParamSize);
Console.Write(i + " iteration. Current best: ");
Program.PrintParameters(BestGenome.Genes);
Console.WriteLine("with fitness: " + BestGenome.Fitness.ToString("G10"));
while (BestGenome.Fitness > _data.MinError && ++i < _data.MaxIterations)
{
lastBest.Copy(BestGenome);
Parallel.For(0, howManyDies, ThreeTournament); // Mortality determines how many times we should do the Tournaments
DetermineBestFitness();
if (!(BestGenome.Fitness < lastBest.Fitness))
{
continue;
}
Console.Write(i + " iteration. Current best: ");
Program.PrintParameters(BestGenome.Genes);
Console.WriteLine("with fitness: " + BestGenome.Fitness.ToString("G10"));
}
return(BestGenome);
}
public static void SingleArithmeticRecombination(Genome firstParent, Genome secondParent, ref Genome firstChild, Random rand)
{
int location = rand.Next(0, firstChild.Genes.Length);
firstChild.Copy(firstParent);
firstChild.Fitness = Single.MaxValue;
firstChild.Genes[location] = Average(firstParent.Genes[location], secondParent.Genes[location]);
}
protected void DeterminePopulationFitness()
{
object syncObject = new object();
Parallel.ForEach(Population, () => new Genome(new float[] {}, float.MaxValue), (genome, loopState, localState) =>
{
DetermineGenomeFitness(ref genome);
return(genome.Fitness < localState.Fitness ? genome : localState);
},
localState =>
{
lock (syncObject)
{
if (localState.Fitness < BestGenome.Fitness)
{
BestGenome.Copy(localState);
}
}
});
}
/// <summary>
/// Breed the population in the world.
/// </summary>
/// <param name="world">The world the population lives in.</param>
/// <param name="population">The population we want to breed.</param>
public static void Breed(SimulationWorld world, Population population)
{
AIEntity[] entitys = population.GetEntitys();
//Bread the top half section to the lower half.
int topAmount = (int)(population.GetAmount() * 0.5f);
for (int i = 0; i < topAmount; i += 2)
{
Genome a = entitys[i].GetGenome();
Genome b = entitys[i + 1].GetGenome();
BreedGenome(world, a, b, i, entitys, population.GetAmount());
entitys[i] = new AIEntity(world, a.Copy());
entitys[i + 1] = new AIEntity(world, b.Copy());
}
}