public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeBinaryStringRaw64 m = mom as GenomeBinaryStringRaw64;
GenomeBinaryStringRaw64 d = dad as GenomeBinaryStringRaw64;
ulong mask = AllOnes << point;
bits = (mask & m.bits) | (~mask & d.bits);
}
public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeBinaryStringRaw32 m = mom as GenomeBinaryStringRaw32;
GenomeBinaryStringRaw32 d = dad as GenomeBinaryStringRaw32;
uint mask = 0xFFFFFFFF << point;
bits = (mask & m.bits) | (~mask & d.bits);
}
public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeBinaryStringRaw64 m = mom as GenomeBinaryStringRaw64;
GenomeBinaryStringRaw64 d = dad as GenomeBinaryStringRaw64;
ulong mask = AllOnes << point;
bits = (mask & m.bits) | (~mask & d.bits);
}
public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeBinaryString m = mom as GenomeBinaryString;
GenomeBinaryString d = dad as GenomeBinaryString;
for (int i = 0; i < bits.Length; i++) {
bits[i] = (i < point ? m : d).bits[i];
}
}
public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeBinaryStringRaw32 m = mom as GenomeBinaryStringRaw32;
GenomeBinaryStringRaw32 d = dad as GenomeBinaryStringRaw32;
uint mask = 0xFFFFFFFF << point;
bits = (mask & m.bits) | (~mask & d.bits);
}
public override float CalculateFitness(BaseGenome genome)
{
BaseGenomeBinary g = genome as BaseGenomeBinary;
int nrOnes = 0;
for (int i = 0; i < g.Length; i++)
nrOnes += g.IsSet(i) ? 1 : 0;
return nrOnes;
}
public void ShouldCreateWithoutParameters()
{
BaseGenome <int, int> genome = new BaseGenome <int, int>();
Assert.IsNotNull(genome);
Assert.IsNotNull(genome.Genes);
Assert.AreEqual(0, genome.Genes.Count);
Assert.AreEqual(0, genome.Value);
}
public virtual void Init(BaseGenome genome)
{
gameObject.transform.rotation = startRotation;
gameObject.transform.position = startPosition;
duration = 0;
isDone = false;
initialized = true;
}
public override void CloneFrom(BaseGenome source)
{
GenomeFloatString s = source as GenomeFloatString;
for (int i = 0; i < floats.Length; i++)
{
floats[i] = s.floats[i];
}
}
public override void CloneFrom(BaseGenome source)
{
GenomeBinaryString s = source as GenomeBinaryString;
for (int i = 0; i < bits.Length; i++)
{
bits[i] = s.bits[i];
}
}
public override void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point)
{
GenomeFloatString m = mom as GenomeFloatString;
GenomeFloatString d = dad as GenomeFloatString;
for (int i = 0; i < floats.Length; i++)
{
floats[i] = (i < point ? m : d).floats[i];
}
}
public override void Init(BaseGenome genome) {
base.Init(genome);
GenomeFloatString gfs = genome as GenomeFloatString;
neuralNet = new NeuralNet(new int[]{5, 8, 3}, gfs.GetArray());
sensors = new float[5+1];
sensors[5] = 0;
totalDistance = 0;
}
public override float CalculateFitness(BaseGenome genome)
{
BaseGenomeBinary g = genome as BaseGenomeBinary;
int nrOnes = 0;
for (int i = 0; i < g.Length; i++)
{
nrOnes += g.IsSet(i) ? 1 : 0;
}
return(nrOnes);
}
public override void Mate(SelectionBuffer selected, Population newPopulation, GeneticAlgorithm.NextStepDelegate callback)
{
// The new population is populated with 2 children each iteration.
for (int i = 0; i < newPopulation.Size; i += 2)
{
BaseGenome mom = selected[Random.Range(0, selected.Size)]; // TODO: mom and dad can be the same..
BaseGenome dad = selected[Random.Range(0, selected.Size)];
int point = Random.Range(0, mom.Length);
newPopulation[i].Genome.OnePointCrossover(mom, dad, point);
newPopulation[i + 1].Genome.OnePointCrossover(mom, dad, point);
}
callback();
}
public override BaseGenome GetBaseGenome()
{
BaseGenome res = new BaseGenome();
// required ones
res.AddKey("baseHunger", 2.0f, 2.0f);
res.AddKey("baseStamina", 2.0f, 2.0f);
res.AddKey("maxStamina", 5.0f, 10.0f);
res.AddKey("maxFood", 5.0f, 10.0f);
// for weighting behaviors
res.AddKey("easilySpooked", 0.5f, 0.9f);
res.AddKey("curiosity", 0.2f, 0.4f);
res.AddKey("extrovertednes", 0.3f, 0.7f);
return(res);
}
public override void Mutate(Population population, GeneticAlgorithm.NextStepDelegate callback)
{
for (int i = 0; i < population.Size; i++)
{
if (Random.value > mutationProbability)
{
continue;
}
BaseGenome genome = population[i].Genome;
int nrMutations = Random.Range(0, (int)Mathf.Round(bitMutationFraction * genome.Length));
for (int j = 0; j < nrMutations; j++)
{
genome.Modify(Random.Range(0, genome.Length));
}
}
callback();
}
public void ShouldCreateWithGenes()
{
List <int> genes = new List <int>();
genes.Add(1);
genes.Add(2);
genes.Add(3);
BaseGenome <int, int> genome = new BaseGenome <int, int>(genes);
Assert.IsNotNull(genome);
Assert.IsNotNull(genome.Genes);
Assert.AreEqual(3, genome.Genes.Count);
Assert.AreEqual(1, genome.Genes[0]);
Assert.AreEqual(2, genome.Genes[1]);
Assert.AreEqual(3, genome.Genes[2]);
Assert.AreEqual(0, genome.Value);
}
/// <summary>
/// Populates the population
/// </summary>
public void Populate()
{
if (BaseGenome == null)
{
throw new Exception("BaseGenome has to be set first!");
}
if (Mutator == null)
{
throw new Exception("Mutator has to be set first!");
}
//Add mutated versions of the base genome
for (var i = 0; i < PopulationSize; i++)
{
FindSpecies(CurrentPopulation, Mutator.Mutate(BaseGenome.Clone()));
}
}
// 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;
}
public override void CloneFrom(BaseGenome a)
{
bits = (a as GenomeBinaryStringRaw32).bits;
}
public abstract void CloneFrom(BaseGenome source);
public override void CloneFrom(BaseGenome a)
{
bits = (a as GenomeBinaryStringRaw32).bits;
}
public virtual float CalculateFitness(BaseGenome genome)
{
return(0);
}
// Mating or crossovers public abstract void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point);
public override void CloneFrom(BaseGenome source)
{
GenomeBinaryString s = source as GenomeBinaryString;
for (int i = 0; i < bits.Length; i++)
bits[i] = s.bits[i];
}
public PhenomeDescription(BaseGenome genome)
{
this.genome = genome;
// phenome =
}
public virtual float CalculateFitness(BaseGenome genome)
{
return 0;
}
// Mating or crossovers public abstract void OnePointCrossover(BaseGenome mom, BaseGenome dad, int point);
public abstract void CloneFrom(BaseGenome source);
public override void CloneFrom(BaseGenome source)
{
GenomeFloatString s = source as GenomeFloatString;
for (int i = 0; i < floats.Length; i++)
floats[i] = s.floats[i];
}
