void Update()
{
if (!simulating)
{
return;
}
// Update cars...
bool everyoneDone = true;
for (int i = 0; i < cars.Length; i++)
{
everyoneDone = everyoneDone && cars[i].IsDone();
if (!everyoneDone)
{
break;
}
}
if (everyoneDone)
{
simulating = false;
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
pd.Fitness = CalculateFitness(cars[i]);
}
CalculateMinMaxTotal(population);
Debug.Log(population.MaxFitness + " " + population.MinFitness + " " + population.TotalFitness);
doneCallback();
}
}
public override void FitnessFunction(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
pd.Fitness = CalculateFitness(pd.Genome as BaseGenomeBinary);
}
CalculateMinMaxTotal(population);
callback();
}
public virtual void FitnessFunctionSimple(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
pd.Fitness = CalculateFitness(pd.Genome);
}
CalculateMinMaxTotal(population);
callback();
}
// TODO: One calculate function with bitmask.
public void CalculateMinMax(Population population)
{
float minFitness = Mathf.Infinity;
float maxFitness = -Mathf.Infinity;
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
maxFitness = Mathf.Max(maxFitness, pd.Fitness);
minFitness = Mathf.Min(minFitness, pd.Fitness);
}
population.MinFitness = minFitness;
population.MaxFitness = maxFitness;
}
public override void FitnessFunction(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
this.population = population;
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
phenomeBuffer[i].Init(pd.Genome);
}
InitiateNewSimulation();
simulating = true;
doneCallback = callback;
}
void InitiateNewSimulation()
{
if (cars != null)
{
for (int i = 0; i < cars.Length; i++)
{
Destroy(cars[i].gameObject);
}
}
cars = new Car[population.Size];
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
cars[i] = Helper.Instansiate <Car>(CarPrefab);
cars[i].Init(pd.Genome as GenomeFloatString);
}
}
public void CalculateMinMaxTotal(Population population)
{
float minFitness = Mathf.Infinity;
float maxFitness = -Mathf.Infinity;
float total = 0;
for (int i = 0; i < population.Size; i++)
{
PhenomeDescription pd = population[i];
float f = pd.Fitness;
maxFitness = Mathf.Max(maxFitness, f);
minFitness = Mathf.Min(minFitness, f);
total += f;
}
population.MinFitness = minFitness;
population.MaxFitness = maxFitness;
population.TotalFitness = total;
}
// Adaptive GAs
// In CAGA (clustering-based adaptive genetic algorithm)
void Start()
{
settings = GetComponent <GASettings>();
populationSize = settings.PopulationSize;
nrGenerations = settings.NumberOfGenerations;
blocking = settings.RunAsFastAsPossible;
environment.Settings = settings;
selectionStrategy.Settings = settings;
matingStrategy.Settings = settings;
mutationStrategy.Settings = settings;
population = new Population(populationSize);
selectionBuffer = new SelectionBuffer(populationSize);
statistics = GetComponent <Statistics>();
if (statistics == null)
{
statistics = gameObject.AddComponent <Statistics>();
}
statistics.Population = population;
BasePhenome phenomeTemplate = Helper.InstansiateAndGet <BasePhenome>(phenomePrefab);
BaseGenome genomeTemplate = phenomeTemplate.Genome;
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
population[i] = new PhenomeDescription(genome);
}
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
selectionBuffer[i] = genome;
}
nextStepDelegate = NextStep;
currentState = State.FitnessTest;
}
// Adaptive GAs
// In CAGA (clustering-based adaptive genetic algorithm)
void Start()
{
settings = GetComponent<GASettings>();
populationSize = settings.PopulationSize;
nrGenerations = settings.NumberOfGenerations;
blocking = settings.RunAsFastAsPossible;
environment.Settings = settings;
selectionStrategy.Settings = settings;
matingStrategy.Settings = settings;
mutationStrategy.Settings = settings;
population = new Population(populationSize);
selectionBuffer = new SelectionBuffer(populationSize);
statistics = GetComponent<Statistics>();
if (statistics == null)
statistics = gameObject.AddComponent<Statistics>();
statistics.Population = population;
BasePhenome phenomeTemplate = Helper.InstansiateAndGet<BasePhenome>(phenomePrefab);
BaseGenome genomeTemplate = phenomeTemplate.Genome;
for (int i = 0; i < populationSize; i++) {
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
population[i] = new PhenomeDescription(genome);
}
for (int i = 0; i < populationSize; i++) {
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
selectionBuffer[i] = genome;
}
nextStepDelegate = NextStep;
currentState = State.FitnessTest;
}
