public void AddIntersection(GeneticIndividual first, GeneticIndividual second, TicTacToeContext context)
{
var newIndividual = new GeneticIndividual
{
GenerationNumber = first.GenerationNumber,
ImportanceOrder = 7,
PlayedGames = 0,
Score = 0,
};
context.Set <GeneticIndividual>().Add(newIndividual);
context.SaveChanges();
var firstFactors =
context.Set <GeneticFactor>()
.Where(factor => factor.GeneticIndividualId == first.GeneticIndividualId)
.ToList();
var secondFactors =
context.Set <GeneticFactor>()
.Where(factor => factor.GeneticIndividualId == second.GeneticIndividualId)
.ToList();
var newFactors = (from firstFactor in firstFactors
join secondFactor in secondFactors on firstFactor.FieldId equals secondFactor.FieldId
select new GeneticFactor
{
Factor = (firstFactor.Factor + secondFactor.Factor) / 2,
FieldId = firstFactor.FieldId,
GeneticIndividualId = newIndividual.GeneticIndividualId
}).ToList();
context.Set <GeneticFactor>().AddRange(newFactors);
context.SaveChanges();
}
public void AddNewMember(GeneticIndividual individual, TicTacToeContext context)
{
var newIndividual = new GeneticIndividual
{
GenerationNumber = individual.GenerationNumber,
ImportanceOrder = 10,
PlayedGames = 0,
Score = 0,
};
context.Set <GeneticIndividual>().Add(newIndividual);
context.SaveChanges();
var factors =
context.Set <GeneticFactor>()
.Where(factor => factor.GeneticIndividualId == individual.GeneticIndividualId)
.ToList();
var newFactors = factors.Select(factor => new GeneticFactor
{
GeneticIndividualId = newIndividual.GeneticIndividualId,
Factor = this.random.Next(0, 100),
FieldId = factor.FieldId
}).ToList();
context.Set <GeneticFactor>().AddRange(newFactors);
context.SaveChanges();
}
private void SwapGenes(GeneticIndividual partner, int num)
{
float aux;
aux = partner.genotype[num];
partner.genotype[num] = this.genotype[num];
this.genotype[num] = aux;
}
public static GeneticIndividual Crossover(GeneticIndividual a, GeneticIndividual b)
{
var ret = new GeneticIndividual(a._recognizer);
for (var i = 0; i < a.Items.Length; i++)
{
ret.Items[i] = GeneticIndividualItem.Crossover(a.Items[i], b.Items[i]);
}
return(ret);
}
public override Individual Clone()
{
GeneticIndividual new_ind = new GeneticIndividual(this.topology, this.maxNumberOfEvaluations, this.mutation);
genotype.CopyTo(new_ind.genotype, 0);
new_ind.fitness = this.Fitness;
new_ind.evaluated = false;
return(new_ind);
}
public override Individual Clone()
{
GeneticIndividual new_ind = new GeneticIndividual(this.topology, this.n_pontos);
genotype.CopyTo(new_ind.genotype, 0);
new_ind.fitness = this.Fitness;
new_ind.evaluated = false;
return(new_ind);
}
public override void InitPopulation()
{
//You should implement the code to initialize the population here
population = new List <Individual>();
while (population.Count < populationSize)
{
GeneticIndividual new_ind = new GeneticIndividual(topology);
new_ind.Initialize();
population.Add(new_ind);
}
}
//The Step function assumes that the fitness values of all the individuals in the population have been calculated.
public override void Step()
{
//criar a nova populacao
List <Individual> new_pop = new List <Individual>();
updateReport(); //called to get some stats
// fills the rest with mutations of the best!
if (elitist)
{
for (int i = 0; i < populationSize - (elistismoValor * populationSize); i++)
{
GeneticIndividual best = (GeneticIndividual) new TournmentSelect().selectIndividuals(population, tournamentSize).Clone();
GeneticIndividual best2 = (GeneticIndividual) new TournmentSelect().selectIndividuals(population, tournamentSize).Clone();
best.Crossover(best2, crossoverProbability);
best.Mutate(mutationProbability);
new_pop.Add(best.Clone());
Debug.Log(new_pop.Count);
}
float max2 = float.MaxValue;
Individual elite = null;
for (int i = 0; i < (elistismoValor * populationSize); i++)
{
float max = float.MinValue;
for (int j = 0; j < populationSize; j++)
{
if (population[j].Fitness > max && population[j].Fitness < max2)
{
max = population[j].Fitness;
elite = (GeneticIndividual)population[j];
}
}
max2 = max;
new_pop.Add(elite.Clone());
}
}
else
{
for (int i = 0; i < populationSize; i++)
{
GeneticIndividual best = (GeneticIndividual) new TournmentSelect().selectIndividuals(population, tournamentSize).Clone();
GeneticIndividual best2 = (GeneticIndividual) new TournmentSelect().selectIndividuals(population, tournamentSize).Clone();
best.Crossover(best2, crossoverProbability);
best.Mutate(mutationProbability);
new_pop.Add(best.Clone());
Debug.Log(new_pop.Count);
}
}
population = new_pop;
generation++;
}
public override void InitPopulation()
{
population = new List <Individual>();
// jncor
while (population.Count < populationSize)
{
GeneticIndividual new_ind = new GeneticIndividual(topology);
new_ind.Initialize();
population.Add(new_ind);
}
}
public override void InitPopulation()
{
population = new List <Individual>();
while (population.Count < populationSize)
{
GeneticIndividual gene = new GeneticIndividual(topology);
gene.Initialize();
population.Add(gene);
}
Debug.Log("-------------------------------INITIALIZATION END -------------------------------");
}
public override void InitPopulation()
{
//You should implement the code to initialize the population
//tournmentsize atribuido ja
this.population = new List <Individual>();
for (int i = 0; i < populationSize; i++)
{
GeneticIndividual a = new GeneticIndividual(this.topology);
a.Initialize();
this.population.Add(a);
}
}
public override void Step()
{
List <Individual> new_pop = new List <Individual>();
updateReport(); //called to get some stats
if (elitist)
{
GeneticIndividual melhor = (GeneticIndividual)overallBest.Clone();
new_pop.Add(melhor); //coloca o melhor de sempre na populaçao
}
while (new_pop.Count < populationSize)
{
GeneticIndividual parent1 = (GeneticIndividual)selection.torneio(population, tournamentSize); //torneio
GeneticIndividual parent2;
do
{
parent2 = (GeneticIndividual)selection.torneio(population, tournamentSize);
} while (parent1 == parent2);//para que o pai 1 seja diferente do 2
GeneticIndividual tmp1 = (GeneticIndividual)parent1.Clone();
GeneticIndividual tmp2 = (GeneticIndividual)parent2.Clone();
tmp1.Crossover(tmp2, crossoverProbability); // crossover em temporarios
tmp2.Crossover(tmp1, crossoverProbability);
GeneticIndividual child1 = tmp1;// apenas para clareza
GeneticIndividual child2 = tmp2;
child1.Mutate(mutationProbability);//mutaçoes
child2.Mutate(mutationProbability);
new_pop.Add(child1); //adiciona à população os filhos
new_pop.Add(child2);
}
population = new_pop;
/*
* float maximum = population[0].Fitness;
* for(int i = 0; i < population.Count - 1; i++)
* {
* if (population[i].Fitness > maximum)
* {
* maximum = population[i].Fitness;
* }
* }
*
* Debug.Log("Max: " + maximum);
*/
generation++;//passa para a próxima geração
}
public override void Crossover(Individual partner, float probability)
{
//Implementar
GeneticIndividual tmp = (GeneticIndividual)partner.Clone();
if (Random.Range(0.0f, 1.0f) < probability)
{
for (int i = 0; i < totalSize / 2; i++)
{
genotype[i] = tmp.genotype[i];
}
}
}
public static GeneticIndividual Mutation(GeneticIndividual a)
{
var ret = new GeneticIndividual(a._recognizer);
var idx = Random.Next(a._recognizer.DigitsCount);
for (var i = 0; i < a.Items.Length; i++)
{
ret.Items[i] = i == idx
? GeneticIndividualItem.Mutation(a.Items[i])
: a.Items[i];
}
return(ret);
}
// Função alterada, original em baixo
public override void InitPopulation()
{
population = new List <Individual>();
Debug.Log("Estou a inicializar a população no Genetic");
// jncor
while (population.Count < populationSize)
{
GeneticIndividual new_ind = new GeneticIndividual(topology);
new_ind.Initialize();
population.Add(new_ind);
}
Debug.Log("População inicializada");
Debug.Log("Valor" + numeroValoresPreservados);
}
public override void InitPopulation()
{
maxNumberOfEvaluations = Mathf.Min(maxNumberOfEvaluations, populationSize);
populationRed = new List <Individual>();
populationBlue = new List <Individual>();
while (populationRed.Count < populationSize)
{
GeneticIndividual new_ind_red = new GeneticIndividual(NNTopology, maxNumberOfEvaluations, mutationMethod)
{
Mean = Mean,
Stdev = Stdev
};
GeneticIndividual new_ind_blue = new GeneticIndividual(NNTopology, maxNumberOfEvaluations, mutationMethod)
{
Mean = Mean,
Stdev = Stdev
};
if (seedPopulationFromFile)
{
NeuralNetwork nnRed = getRedIndividualFromFile();
NeuralNetwork nnBlue = getBlueIndividualFromFile();
new_ind_red.Initialize(nnRed);
new_ind_blue.Initialize(nnBlue);
//only the first individual is an exact copy. the other are going to suffer mutations
if (populationRed.Count != 0 && populationBlue.Count != 0)
{
new_ind_red.Mutate(mutationProbabilityRedPopulation);
new_ind_blue.Mutate(mutationProbabilityBluePopulation);
}
}
else
{
new_ind_red.Initialize();
new_ind_blue.Initialize();
}
populationRed.Add(new_ind_red);
populationBlue.Add(new_ind_blue);
}
switch (selectionMethod)
{
case SelectionType.Tournament:
selection = new TournamentSelection(tournamentSize);
break;
}
}
//The Step function assumes that the fitness values of all the individuals in the population have been calculated.
public override void Step()
{
System.Random random = new System.Random();
//You should implement the code runs in each generation here
List <Individual> new_pop = new List <Individual>();
updateReport();
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
population.Reverse();
if (elitist == true)
{
for (int i = 0; i < elitistSize; i++)
{
new_pop.Add(population[i].Clone());
}
}
if (tournamentSize > 0)
{
List <Individual> winners = fitnessSelect.selectIndividuals(population, tournamentSize, tournamentProbability);
for (int i = new_pop.Count; i < population.Count; i++)
{
Individual parent1 = winners[Random.Range(0, winners.Count)];
winners.Remove(parent1);
Individual parent2 = winners[Random.Range(0, winners.Count)].Clone();
parent2.Crossover(parent1, crossoverProbability);
parent2.Mutate(mutationProbability);
winners.Add(parent1);
new_pop.Add(parent2);
}
}
else
{
for (int i = new_pop.Count; i < populationSize; i++)
{
GeneticIndividual tmp = (GeneticIndividual)overallBest.Clone();
tmp.Mutate(mutationProbability);
new_pop.Add(tmp);
}
}
population = new_pop;
generation++;
}
public void MakeNewPopulation()
{
List<GeneticIndividual> newIndividuals = new List<GeneticIndividual>();
// TODO make the genetic stuff and crossover etc
// temp, just make a new population
for (int i = 0 ; i < individuals.Count ; i++)
{
GeneticIndividual child = new GeneticIndividual();
child.SetFromParents(individuals[i], individuals[i]);
newIndividuals.Add(child);
}
individuals.Clear();
individuals.AddRange(newIndividuals);
}
//The Step function assumes that the fitness values of all the individuals in the population have been calculated.
public override void Step()
{
List <Individual> new_pop = new List <Individual> ();
List <Individual> elitist_list = new List <Individual> ();
if (elitist)
{
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
Debug.Log("P: ");
for (int i = 0; i < populationSize; i++)
{
Debug.Log(population [i].Fitness);
}
for (int i = 0; i < n_elitists; i++)
{
int pop = populationSize - 1;
elitist_list.Add(population [pop - i].Clone());
}
}
updateReport(); //called to get some stats
// fills the rest with mutations of the best !
for (int i = 0; i < populationSize; i++)
{
GeneticIndividual pai = (GeneticIndividual)Tournament(population, tournamentSize).Clone();
GeneticIndividual pai2 = (GeneticIndividual)Tournament(population, tournamentSize).Clone();
pai.Crossover(pai2.Clone(), crossoverProbability, numerocortes);
pai.Mutate(mutationProbability);
new_pop.Add(pai.Clone());
}
population = new_pop;
if (elitist)
{
for (int i = 0; i < n_elitists; i++)
{
Individual pick_to_delete = new_pop [Random.Range(0, populationSize - 1)];
new_pop.Remove(pick_to_delete);
new_pop.Add(elitist_list [i].Clone());
}
}
generation++;
}
public void DoIteration()
{
int size = Individuals.Count;
for (var i = 0; i < size; i++)
{
for (var j = 0; j < i; j++)
{
Individuals.Add(GeneticIndividual.Crossover(Individuals[i], Individuals[j]));
}
for (var j = 0; j < 100; j++)
{
Individuals.Add(GeneticIndividual.Mutation(Individuals[i]));
}
}
Individuals = Individuals
.Where(x => !x.IsInvalid())
.OrderBy(x => x.Fitness())
.Reverse()
.Take(MaxSize)
.ToList();
}
public void AddMutation(GeneticIndividual individual, TicTacToeContext context)
{
var newIndividual = new GeneticIndividual
{
GenerationNumber = individual.GenerationNumber,
ImportanceOrder = 4,
PlayedGames = 0,
Score = 0,
};
context.Set <GeneticIndividual>().Add(newIndividual);
context.SaveChanges();
var factors =
context.Set <GeneticFactor>()
.Where(factor => factor.GeneticIndividualId == individual.GeneticIndividualId)
.ToList();
var newFactors = factors.Select(factor => new GeneticFactor
{
GeneticIndividualId = newIndividual.GeneticIndividualId,
Factor = this.winFactors.ContainsKey(factor.FieldId)
? factor.Factor + this.winFactors[factor.FieldId] * 5
: factor.Factor + this.random.Next(11) - 5,
FieldId = factor.FieldId
})
.Select(factor => new GeneticFactor
{
GeneticIndividualId = factor.GeneticIndividualId,
Factor = factor.Factor > 100 ? 100 : factor.Factor < 0 ? 0 : factor.Factor,
FieldId = factor.FieldId
}).ToList();
context.Set <GeneticFactor>().AddRange(newFactors);
context.SaveChanges();
}
public GeneticPopolation(CaptchaRecognizer recognizer, int maxSize)
{
_recognizer = recognizer;
MaxSize = maxSize;
var consecutiveInvalidCount = 0;
while (Individuals.Count < MaxSize)
{
var nxt = GeneticIndividual.Mutation(new GeneticIndividual(_recognizer, true));
if (!nxt.IsInvalid())
{
consecutiveInvalidCount = 0;
Individuals.Add(nxt);
}
else
{
consecutiveInvalidCount++;
if (consecutiveInvalidCount > MaxConsecutiveFailures)
{
throw new CantSplitDigitsException();
}
}
}
}
public void SetFromParents(GeneticIndividual a, GeneticIndividual b)
{
// todo
fitnessHACK = (a.GetFitness() + b.GetFitness()) / 2;
}
