Update()
{
if (!initalized)
{
return;
}
if (runningSimulation || runOnlyMode)
{
bool keepRunning = false;
for (int i = 0; i < brains.Count; i++)
{
CarBrain brain = brains[i];
if (brain.isAlive)
{
keepRunning = true;
if (brain.Fitness() > best.Fitness() || !best.isAlive)
{
SetBestCar(brain);
}
}
}
if (!keepRunning)
{
if (runOnlyMode)
{
for (int i = 0; i < brains.Count; i++)
{
CarBrain brain = brains[i];
brain.Reset();
}
}
else
{
// If all genomes are dead we stop the simulation.
runningSimulation = false;
}
}
}
if (!runningSimulation && !runOnlyMode)
{
generationCount++;
// Keep track of the all time best phenotype
if (best.Fitness() > population.BestFitness())
{
bestPhenotype = best.Phenotype();
}
/*
* Using the genetic algorithm try to improve the brains for each
* genome in the hope that it will perform better.
*/
phenotypes = population.Epoch(GetFitnessScores());
// TODO: clean up the mess below
Dictionary <int, CGenome> genomeIDs = new Dictionary <int, CGenome>();
foreach (CGenome genome in population.Genomes())
{
genomeIDs.Add(genome.ID(), genome);
}
List <CarBrain> brainsToBeAssigned = new List <CarBrain> (brains);
for (int i = brainsToBeAssigned.Count - 1; i >= 0; i--)
{
int oldGenomeID = brainsToBeAssigned[i].genomeID;
if (genomeIDs.ContainsKey(oldGenomeID) && genomeIDs[oldGenomeID].Phenotype() != null)
{
brainsToBeAssigned[i].AssignPhenotype(genomeIDs[oldGenomeID].Phenotype());
genomeIDs.Remove(oldGenomeID);
brainsToBeAssigned[i].GetComponent <Car>().id.text = "" + brains[i].genomeID;
brainsToBeAssigned[i].Reset();
brainsToBeAssigned.RemoveAt(i);
}
}
for (int i = 0; i < brainsToBeAssigned.Count; i++)
{
brainsToBeAssigned[i].genomeID = genomeIDs.ElementAt(0).Key;
brainsToBeAssigned[i].AssignPhenotype(genomeIDs.ElementAt(0).Value.Phenotype());
genomeIDs.Remove(genomeIDs.ElementAt(0).Key);
brainsToBeAssigned[i].GetComponent <Car>().id.text = "" + brains[i].genomeID;
brainsToBeAssigned[i].Reset();
}
generation.text = "Generation:\t" + generationCount;
bestFitness.text = string.Format("Best fitness:\t{0:#.0000}", population.BestFitness());
Dictionary <int, int> allSpecies = new Dictionary <int, int> ();
CSpecies theBestSpecies = null;
float bestSpeciesFitness = -1;
for (int i = 0; i < population.Species().Count; i++)
{
CSpecies species = population.Species()[i];
allSpecies.Add(species.ID(), species.NumMembers());
if (species.BestFitness() > bestSpeciesFitness)
{
bestSpeciesFitness = species.BestFitness();
theBestSpecies = species;
}
if (species.Leader() == null)
{
continue;
}
}
numSpecies.text = "Num species:\t" + allSpecies.Count;
bestSpecies.text = "Best species:\t" + theBestSpecies.ID();
numMembers.text = "Num members:\t" + theBestSpecies.NumMembers();
speciesAge.text = "Species age:\t" + theBestSpecies.Age();
gensWithoutImproving.text = "Gens no impr:\t" + theBestSpecies.GensNoImprovement();
bar.UpdateBar(allSpecies);
runningSimulation = true;
}
}
AssignPhenotype(CNeuralNet phenotype)
{
this.phenotype = phenotype;
}
