void RunEpoch()
{
if (crossover.PopulationOrParents)
{
// działam na całej populacji
crossover.Cross(population, r);
}
else
{
// działam na wybranych rodzicach
// musze wygenerowac nową populacje
Organism[] newPopulation = new Organism[population.Length];
for (int i = 0; i < population.Length; i++)
{
newPopulation[i] = CreateChild(r);
}
population = newPopulation;
}
// sprawdzamy czy selekcja wymaga posortowanej populacji
if (selectionType.NeedSortedPopulation)
{
Array.Sort(population);
}
currentEpoch++;
// aktualizacja najlepszego osobnika
UpdateBest();
stats.AfterEpoch();
}
public static void CreateBabyHawk(HawkDNA motherDNA, HawkDNA fatherDNA, Transform mother)
{
HawkDNA dna = new HawkDNA();
Mutation mutation = new Mutation();
Crossover crossover = new Crossover();
dna.speed = mutation.MutateGene(crossover.Cross(motherDNA.speed, fatherDNA.speed), DNA.SPEED_MIN, DNA.SPEED_MAX);
dna.foodViewRange = mutation.MutateGene(crossover.Cross(motherDNA.foodViewRange, fatherDNA.foodViewRange), DNA.FOODVIEWRANGE_MIN, DNA.FOODVIEWRANGE_MAX);
dna.waterViewRange = mutation.MutateGene(crossover.Cross(motherDNA.waterViewRange, fatherDNA.waterViewRange), DNA.WATERVIEWRANGE_MIN, DNA.FOODVIEWRANGE_MAX);
var hawk = Instantiate(Instance.babyHawk, mother.position, Quaternion.identity).GetComponent <BabyHawk>();
AnimalStatistics.Instance.AddAnimal(hawk.matureHawk);
hawk.Initialize(dna);
}
/// <summary>
/// Crosses the specified parents.
/// </summary>
/// <param name="parents">The parents.</param>
/// <returns>The result chromosomes.</returns>
private IList <IChromosome> Cross(IList <IChromosome> parents)
{
var offspring = new List <IChromosome>();
for (int i = 0; i < Population.MinSize; i += Crossover.ParentsNumber)
{
var selectedParents = parents.Skip(i).Take(Crossover.ParentsNumber).ToList();
// If match the probability cross is made, otherwise the offspring is an exact copy of the parents.
// Checks if the number of selected parents is equal which the crossover expect, because the in the end of the list we can
// have some rest chromosomes.
if (selectedParents.Count == Crossover.ParentsNumber && RandomizationProvider.Current.GetDouble() <= CrossoverProbability)
{
offspring.AddRange(Crossover.Cross(selectedParents));
}
}
if (_debugging)
{
Console.WriteLine("{0} parents created {1} children", parents.Count, offspring.Count);
if (offspring.Count < parents.Count)
{
Console.WriteLine("Time for debugging! This is a bp");
}
}
return(offspring);
}
protected override IList <IGenome> DoProduction(
IList <IGenome> sampleGenomes,
GenomeProductionSession thisSession,
GenomeProductionSession totalSession)
{
var selections = (int)Math.Ceiling((float)thisSession.requiredNb / Crossover.NbOfChildren);
var totalNbToSelct = selections * Crossover.NbOfParents;
Selection.Prepare(
sampleGenomes,
thisSession,
totalSession,
totalNbToSelect: totalNbToSelct);
Crossover.Prepare(
sampleGenomes,
thisSession,
totalSession);
while (thisSession.CurrentlyProduced.Count < thisSession.requiredNb)
{
var parents = Selection.Select(Crossover.NbOfParents).ToArray();
var children = Crossover.Cross(parents);
var usedChildren = thisSession.AddNewProducedGenomes(children);
foreach (var child in usedChildren)
{
this.MutationManager.ApplyMutations(child);
}
}
return(thisSession.CurrentlyProduced);
}
/// <summary>
/// Crosses the specified parents.
/// </summary>
/// <param name="parents">The parents.</param>
/// <returns>The result chromosomes.</returns>
private IList <IChromosome> Cross(IList <IChromosome> parents)
{
var offspring = new List <IChromosome>();
//Console.WriteLine("Crossover probability: {0}", CrossoverProbability);
for (int i = 0; i < Population.MinSize; i += Crossover.ParentsNumber)
{
var selectedParents = parents.Skip(i).Take(Crossover.ParentsNumber).ToList();
double randomDouble = ThreadSafeRandom.ThisThreadsRandom.NextDouble();
//Console.WriteLine("Random Number generated to Cross or Not: {0}", randomDouble);
// If match the probability cross is made, otherwise the offspring is an exact copy of the parents.
// Checks if the number of selected parents is equal which the crossover expect, because the in the end of the list we can
// have some rest chromosomes.
if (selectedParents.Count == Crossover.ParentsNumber && randomDouble <= CrossoverProbability)
{
offspring.AddRange(Crossover.Cross(selectedParents));
}
//else
//{
// Console.WriteLine("No Crossover operation is done");
//}
}
return(offspring);
}
public static void CreateBabyMonkey(MonkeyDNA motherDNA, MonkeyDNA fatherDNA, Transform mother)
{
MonkeyDNA dna = new MonkeyDNA();
Mutation mutation = new Mutation();
Crossover crossover = new Crossover();
dna.speed = mutation.MutateGene(crossover.Cross(motherDNA.speed, fatherDNA.speed), DNA.SPEED_MIN, DNA.SPEED_MAX);
dna.foodViewRange = mutation.MutateGene(crossover.Cross(motherDNA.foodViewRange, fatherDNA.foodViewRange), DNA.FOODVIEWRANGE_MIN, DNA.FOODVIEWRANGE_MAX);
dna.waterViewRange = mutation.MutateGene(crossover.Cross(motherDNA.waterViewRange, fatherDNA.waterViewRange), DNA.WATERVIEWRANGE_MIN, DNA.FOODVIEWRANGE_MAX);
dna.sex = mutation.RandomizeSex();
var monkey = Instantiate(Instance.babyMonkey, mother.position, Quaternion.identity).GetComponent <BabyMonkey>();
AnimalStatistics.Instance.AddAnimal(monkey.matureMaleMonkey);
monkey.Initialize(dna);
}
public override void NextGeneration()
{
var nextGenSize = ResizePolicy.NextGenSize(this);
var nextGeneration = new IIndividual[nextGenSize];
for (var i = 0; i < nextGenSize / Crossover.ChildrenCount; i++)
{
var parentalGenotypes = SelectParentalGenotypes();
var genotypes = Crossover.Cross(parentalGenotypes);
for (var j = 0; j < genotypes.Length; j++)
{
var child = IndividualFactory.CreateFromGenotype(genotypes[j]);
Mutation.Mutate(child.Genotype, MutationPolicy, this);
if (!IncompatibilityPolicy.IsCompatible(this, child))
{
child = IncompatibilityPolicy.GetReplacement(this, child, parentalGenotypes);
}
nextGeneration[i * Crossover.ChildrenCount + j] = child;
}
}
var childrenCountDifference = nextGenSize % Crossover.ChildrenCount;
if (childrenCountDifference > 0)
{
var parentalGenotypes = SelectParentalGenotypes();
var genotypes = Crossover.Cross(parentalGenotypes);
var start = nextGenSize - childrenCountDifference;
for (var j = 0; j < childrenCountDifference; j++)
{
var child = IndividualFactory.CreateFromGenotype(genotypes[j]);
Mutation.Mutate(child.Genotype, MutationPolicy, this);
if (!IncompatibilityPolicy.IsCompatible(this, child))
{
child = IncompatibilityPolicy.GetReplacement(this, child, parentalGenotypes);
}
nextGeneration[start + j] = child;
}
}
Individuals = nextGeneration;
DeprecateData();
UpdatePerGenerationData();
if (Generation % 1000 == 0)
{
GC.Collect();
}
}
public override DecimalArrayChromosome FindOptimum()
{
var best = new DecimalArrayChromosome(Constants.ChromosomeSize)
{
Fitness = decimal.MaxValue
};
for (var i = 0; i < IterationLimit; i++)
{
var selectedFromPopulation = Selection.Select(Population);
var childChromosome = Crossover.Cross(selectedFromPopulation[0], selectedFromPopulation[1]);
childChromosome = Mutation.Mutate(childChromosome);
childChromosome.Fitness = FitnessFunction.GetValue(childChromosome);
Population.Remove(selectedFromPopulation[2]);
Population.Add(childChromosome);
var populationBest = decimal.MaxValue;
var bestIndex = 0;
var index = 0;
foreach (var chromosome in Population)
{
if (chromosome.Fitness < populationBest)
{
populationBest = chromosome.Fitness;
bestIndex = index;
}
index++;
}
if (populationBest < best.Fitness)
{
best = Population.GetAtIndex(bestIndex);
Console.WriteLine("Iteration: " + i + ", Best fitness: " + populationBest + ", " + best);
}
if (populationBest < FitnessTerminator)
{
break;
}
}
return(best);
}
public override Population GetNextGeneration(Population population)
{
Population nextGen = new Population(population.Size, population.AllowDuplicates);
nextGen.Add(population.GetFittest(Elitism).ToArray());
int duplicates = 0;
while (!nextGen.IsFull)
{
IList <Individual> children = new List <Individual>();
// create child by crossover or copy from old population
double rand = RandomValueGenerator.Instance.GetDouble();
if (rand < CrossoverRate)
{
var individual1 = Selector.Select(population);
var individual2 = Selector.Select(population);
foreach (var child in Crossover.Cross(individual1, individual2))
{
children.Add(new Individual(child));
}
}
else if (rand - CrossoverRate < MutationRate)
{
var child = new Individual(Selector.Select(population));
if (Mutator.Mutate(child))
{
child.FitnessEvaluated = false;
}
children.Add(child);
}
else
{
children.Add(new Individual(Selector.Select(population)));
}
foreach (var child in children)
{
int added = nextGen.Add(child);
duplicates += 1 - added;
}
}
if (!AllowDuplicates)
{
Logger.WriteLine(2, "Generated duplicates: " + duplicates);
}
return(nextGen);
}
public override DecimalArrayChromosome FindOptimum()
{
var best = new DecimalArrayChromosome(Constants.ChromosomeSize)
{
Fitness = decimal.MaxValue
};
for (var i = 0; i < IterationLimit; i++)
{
var nextGeneration = new Population <DecimalArrayChromosome>(PopulationSize);
if (Elitism)
{
nextGeneration.Add(GetPopulationBest());
}
while (nextGeneration.Count() < PopulationSize)
{
var selectedFromPopulation = Selection.Select(Population);
var childChromosome =
Crossover.Cross(selectedFromPopulation.First(), selectedFromPopulation[1]);
childChromosome = Mutation.Mutate(childChromosome);
childChromosome.Fitness = FitnessFunction.GetValue(childChromosome);
nextGeneration.Add(childChromosome);
}
Population = nextGeneration;
var populationBest = GetPopulationBest();
if (populationBest.Fitness < best.Fitness)
{
best = populationBest;
Console.WriteLine("Iteration: " + i + ", Best fitness: " + populationBest.Fitness + ", " + best);
}
if (populationBest.Fitness < FitnessTerminator)
{
break;
}
}
return(best);
}
public override Chromosome FindOptimum()
{
var best = new Chromosome(ChromosomeSize)
{
Fitness = double.NegativeInfinity
};
for (var i = 0; i < IterationLimit; i++)
{
var selectedFromPopulation = Selection.Select(Population);
var childChromosome = Crossover.Cross(selectedFromPopulation[0], selectedFromPopulation[1]);
childChromosome = Mutation.Mutate(childChromosome);
childChromosome.Fitness = FitnessFunction.CalculateFitness(childChromosome);
Population.Remove(selectedFromPopulation[2]);
Population.Add(childChromosome);
var populationBest = 0.0;
var bestIndex = 0;
var index = 0;
foreach (var chromosome in Population)
{
if (chromosome.Fitness > populationBest)
{
populationBest = chromosome.Fitness;
bestIndex = index;
}
index++;
}
if (populationBest > best.Fitness)
{
best = Population.ElementAt(bestIndex);
Console.WriteLine("Iteration: " + i + ", Best fitness: " + populationBest);
}
}
return(best);
}
public void Start()
{
#region Формирование начальной популяции
Population.FillPopulation(); // Запоним рандомными значениями
#endregion
int currentGeneration = 0;
var selectedIndividuals = new List <IIndividual>();
//var childPop = new Population(PopulationSize);
Random rand = new Random();
while (currentGeneration < GenerationNumber)
{
#region Поколение
currentGeneration += 1;
RunSimulation();
#region Оценивание популяции
foreach (var individual in Population.Individuals)
{
individual.CalcFitness();
}
Population.Individuals.Sort();
PrintGeneration(currentGeneration);
//fitness = Population.Individuals.First().Fitness;
#endregion
#region Селекция
selectedIndividuals = Selection.Select(Population);
#endregion
#region Скрещивание
Population.Individuals.Clear();
while (Population.Individuals.Count < PopulationSize)
{
var parent1 = selectedIndividuals[rand.Next(0, selectedIndividuals.Count)];
var parent2 = selectedIndividuals[rand.Next(0, selectedIndividuals.Count)];
if (rand.NextDouble() < CrossRate)
{
Crossover.Cross(parent1, parent2);
Population.Add(Crossover.Child1);
Population.Add(Crossover.Child2);
}
else
{
Population.Add(parent1);
Population.Add(parent2);
}
}
#endregion
#region Мутация
foreach (var ind in Population.Individuals)
{
for (int i = 0; i < ind.Genes.Count; i++)
{
if (rand.NextDouble() < MutationRate)
{
ind.Mutate(i);
}
}
}
#endregion
// Либо так,
//либо getRange'ем скопировать элементы в pop,
//либо ForEach делегатом пробежаться
//pop = childPop;
#endregion
}
PrintGeneration(currentGeneration);
}
public override Chromosome FindOptimum()
{
var best = new Chromosome(busStops: BusStops, students: Students)
{
Fitness = double.MaxValue
};
for (var i = 0; i < IterationLimit; i++)
{
var selectedFromPopulation = Selection.Select(Population);
var childChromosome = Crossover.Cross(selectedFromPopulation[0], selectedFromPopulation[1]);
childChromosome = Mutation.Mutate(childChromosome);
childChromosome.Fitness = FitnessFunction.CalculateFitness(childChromosome);
Population.Remove(selectedFromPopulation[2]);
Population.Add(childChromosome);
var populationBest = double.MaxValue;
var bestIndex = 0;
var index = 0;
foreach (var chromosome in Population)
{
if (chromosome.Fitness < populationBest)
{
populationBest = chromosome.Fitness;
bestIndex = index;
}
index++;
}
if (populationBest < best.Fitness)
{
best = Population.ElementAt(bestIndex);
Console.WriteLine("Iteration: " + i + ", Best fitness: " + populationBest);
}
if (i % 1000 == 0)
{
Console.WriteLine("Iteration: " + i);
foreach (var stop in best.BusStops)
{
Console.Write(stop.SeatsTaken + ", ");
}
Console.WriteLine();
Console.WriteLine(_stopwatch.Elapsed);
}
if (_stopwatch.Elapsed >= TimeSpan.FromMinutes(1) && _stopwatch.Elapsed < TimeSpan.FromMinutes(5) && !minuteMark)
{
var instanceName = InstanceFilePath.Split('/').Last().Split('.')[0];
var lastDirectoryIndex = InstanceFilePath.LastIndexOf('/');
var instanceDirectory = InstanceFilePath.Substring(0, lastDirectoryIndex + 1);
HelperMethods.WriteToFile(best.ToString(), instanceDirectory + FileNamePrefix + "-1m-" + instanceName + FileNamePostFix);
minuteMark = true;
Console.WriteLine("Fitness value => " + best.Fitness + "\tEvaluation count => " + FitnessFunction.EvaluationCounter);
}
if (_stopwatch.Elapsed >= TimeSpan.FromMinutes(5) && !fiveMinuteMark)
{
var instanceName = InstanceFilePath.Split('/').Last().Split('.')[0];
var lastDirectoryIndex = InstanceFilePath.LastIndexOf('/');
var instanceDirectory = InstanceFilePath.Substring(0, lastDirectoryIndex + 1);
HelperMethods.WriteToFile(best.ToString(), instanceDirectory + FileNamePrefix + "-5m-" + instanceName + FileNamePostFix);
fiveMinuteMark = true;
Console.WriteLine("Fitness value => " + best.Fitness + "\tEvaluation count => " + FitnessFunction.EvaluationCounter);
}
if (populationBest < FitnessTerminator)
{
break;
}
}
_stopwatch.Stop();
if (!fiveMinuteMark)
{
var instanceName = InstanceFilePath.Split('/').Last().Split('.')[0];
var lastDirectoryIndex = InstanceFilePath.LastIndexOf('/');
var instanceDirectory = InstanceFilePath.Substring(0, lastDirectoryIndex + 1);
HelperMethods.WriteToFile(best.ToString(), instanceDirectory + FileNamePrefix + "-5m-" + instanceName + FileNamePostFix);
Console.WriteLine("Fitness value => " + best.Fitness + "\tEvaluation count => " + FitnessFunction.EvaluationCounter);
}
Console.WriteLine("Fitness value => " + best.Fitness + "\tEvaluation count => " + FitnessFunction.EvaluationCounter);
return(best);
}
