private void UpdateFitnesses(Parameters key, double fitness)
{
Load();//update the fitness table from possible other executions
ParameterFitness pf = new ParameterFitness();
if (fitnesses.ContainsKey(key))
{
pf = fitnesses[key];
}
pf.AddValue(fitness);
fitnesses[key] = pf;
}
public IEnumerator RunGeneration()
{
for (int i = 0; i < population.Length; i++)
{
ParameterFitness pf = new ParameterFitness();
if (fitnesses.ContainsKey(population[i]))
{
pf = fitnesses[population[i]];
if (i > 1 || (2 * pf.GetVariance() / Math.Sqrt(pf.n) < 0.1 * pf.average && r.Next(pf.n + 1) != 0))
{
continue;
}
}
SimulationInitializer.SetSimulation(population[i], RUNNING_SPEED);
simulationTimer.Restart();
while (simulationTimer.Elapsed.TotalSeconds < singleSimulationTime)
{
yield return(new WaitForSeconds(1));
_current_simulation_timer = (int)simulationTimer.Elapsed.TotalSeconds;
}
double fitness = GetFitness();
UpdateFitnesses(population[i], fitness);
if (_current_best_fitness < fitness)
{
_current_best_fitness = fitness;
_current_best_fitness_variance = fitnesses[population[i]].GetVariance();
}
text.text = "i=" + i + " gen: " + _generation + " timer:" + _current_simulation_timer + " fitness = " + fitness + "avg:" + pf.average + " var=" + pf.GetVariance() + " n:" + pf.n + " Z:" + (2 * pf.GetVariance() / Math.Sqrt(pf.n)) + "<" + (0.1 * pf.average) + " best:" + _current_best_fitness + " bestVar:" + _current_best_fitness_variance;
Save();
}
CreateNewGeneration();
completed = true;
}
