public void Evaluate(float[] scores)
{
// reset everything
foreach (Species s in species)
{
s.ResetSpecies(new System.Random());
}
scoreMap.Clear();
speciesMap.Clear();
nextGenGenomes.Clear();
highestScore = float.MinValue;
fittestGenome = null;
// place genomes into species
foreach (Genome g in genomes)
{
bool foundSpecies = false;
foreach (Species s in species)
{
if (Genome.CompatibilityDist(g, s.mascot, c1, c2, c3) < dt)
{
s.members.Add(g);
speciesMap.Add(g, s);
foundSpecies = true;
break;
}
}
if (!foundSpecies) // make new species
{
Species newSpecies = new Species(g);
species.Add(newSpecies);
speciesMap.Add(g, newSpecies);
}
}
// remove dead species
foreach (Species s in species.ToList())
{
if (s.members.Count == 0)
{
species.Remove(s);
}
}
if (species.Count > 11)
{
dt += 0.04f;
ConsoleLogger.Log("dt: " + dt.ToString());
}
else if (species.Count < 9)
{
dt -= 0.04f;
ConsoleLogger.Log("dt: " + dt.ToString());
}
ConsoleLogger.Log("No. species: " + species.Count.ToString());
// evaluate genomes and assign fitness
foreach (Genome g in genomes)
{
Species s = speciesMap[g];
float score = scores.ToList()[genomes.IndexOf(g)];
float adjustedScore = score / speciesMap[g].members.Count;
s.AddAdjustedFitness(adjustedScore);
s.fitnessPop.Add(new FitnessGenome(g, adjustedScore));
scoreMap.Add(g, adjustedScore);
if (score > highestScore)
{
highestScore = score;
fittestGenome = g;
}
}
// put best genomes from species into next generation
foreach (Species s in species)
{
// sort genomes by fitness
s.fitnessPop.Sort(FitnessGenomeComparator.CompareTo);
s.fitnessPop.Reverse();
FitnessGenome fittestInSpecies = s.fitnessPop[0];
nextGenGenomes.Add(fittestInSpecies.genome);
}
Random rand = new Random();
// breed genomes
while (nextGenGenomes.Count < populationSize)
{
Species s = GetRandomSpeciesBiasedAdjustedFitness(rand);
Genome p1 = GetRandomGenomeBiasedAdjustedFitness(s, rand);
if (rand.NextDouble() < _INTER_SPECIES_RATE)
{
s = GetRandomSpeciesBiasedAdjustedFitness(rand);
}
Genome p2 = GetRandomGenomeBiasedAdjustedFitness(s, rand);
Genome child;
if (scoreMap[p1] >= scoreMap[p2])
{
child = Genome.Crossover(p1, p2);
}
else
{
child = Genome.Crossover(p2, p1);
}
if (rand.NextDouble() < _MUTATION_RATE)
{
child.ValueMutation();
}
if (rand.NextDouble() < _ADD_CONONECTION_RATE)
{
child.AddConnectionMutation();
}
if (rand.NextDouble() < _ADD_NODE_RATE)
{
child.AddNodeMutation();
}
nextGenGenomes.Add(child);
}
genomes = nextGenGenomes;
nextGenGenomes = new List <Genome>();
}