public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network), EvolutionAlgorithm.MIN_GENOME_FITNESS);
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
}
public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
BehaviorType behavior;
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network, out behavior), EvolutionAlgorithm.MIN_GENOME_FITNESS);
g.RealFitness = g.Fitness;
g.Behavior = behavior;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
if (ea.NeatParameters.noveltySearch)
{
if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
{
ea.CalculateNovelty();
}
}
}