private Individual[] MutatePopulation()
{
int populationSize = population.Length;
for (int i = elitism; i < populationSize; i++)
{
population[i].gen = Genoma.Mutate(r, population[i].gen, mutationRate, maxPerturbation);
}
return(population);
}
public void Initialize(MendelMachine mendelMachine, Genoma genoma,
ActivationFunction activationFunction,
bool learningPhase, float lifeTime, int index)
{
fitness = 0;
this.activationFunction = activationFunction;
this.index = index;
this.mendelMachine = mendelMachine;
this.learningPhase = learningPhase;
this.lifeTime = lifeTime;
perceptron = new Perceptron(genoma, activationFunction);
}
public static Genoma Mutate(Random r, Genoma gen,
float mutationRate, float maxPerturbation)
{
for (int layer = 0; layer < gen.W.Length; layer++)
{
double[,] m = gen.W[layer];
Matrix.MatrixLoop((i, j) =>
{
if (r.NextDouble() < mutationRate)
{
m[i, j] += (r.NextDouble() * 2f - 1f) * maxPerturbation;
}
}, gen.W[layer].X, gen.W[layer].Y);
gen.W[layer] = m;
}
return(gen);
}
public static Genoma Mutate(
Random r,
Genoma gen,
float stdDevMutatuion,
float maxPerturbation
)
{
for (int layer = 0; layer < gen.W.Length; layer++)
{
double[, ] m = gen.W[layer];
Matrix.MatrixLoop((i, j) => {
m[i, j] += GaussianSystemRandom(r, stdDevMutatuion, maxPerturbation);
}, gen.W[layer].X, gen.W[layer].Y);
gen.W[layer] = m;
}
return(gen);
}
private Individual[] CrossPopulation()
{
var tempPopulation = (Individual[])population.Clone();
int populationSize = tempPopulation.Length;
List <Individual> newpop = new List <Individual> ();
for (int i = 0; i < elitism; i++)
{
newpop.Add(tempPopulation[i]);
}
for (int i = 0; i < newIndividuals; i++)
{
newpop.Add(GenerateIndividual());
}
if (sexualMultiplication)
{
for (int i = 0; i < populationSize - newIndividuals - elitism; i++)
{
var p1 = poolSelection(tempPopulation);
var p2 = poolSelection(tempPopulation);
Individual individual = new Individual {
gen = Genoma.Cross(r, p1.gen, p2.gen),
fitness = 0
};
newpop.Add(individual);
}
}
else
{
for (int i = 0; i < populationSize - newIndividuals - elitism; i++)
{
var p1 = poolSelection(tempPopulation);
Individual individual = new Individual()
{
gen = new Genoma(p1.gen.W),
fitness = 0
};
newpop.Add(individual);
}
}
return(newpop.ToArray());
}
private Individual[] CrossPopulation()
{
int populationSize = population.Length;
List <Individual> crosspop = new List <Individual>();
for (int i = 0; i < populationSize; i++)
{
population[i].fitness = -1;
for (int j = 0; j < populationSize - i; j++)
{
crosspop.Add(population[i]);
}
}
List <Individual> newpop = new List <Individual>();
for (int i = 0; i < elitism; i++)
{
newpop.Add(population[i]);
}
for (int i = 0; i < newIndividuals; i++)
{
newpop.Add(GenerateIndividual());
}
for (int i = 0; i < populationSize - newIndividuals - elitism; i++)
{
Individual individual = new Individual
{
gen = Genoma.Cross(r,
crosspop[Random.Range(0, crosspop.Count)].gen,
crosspop[Random.Range(0, crosspop.Count)].gen),
fitness = 0
};
newpop.Add(individual);
}
return(newpop.ToArray());
}
public static Genoma Cross(Random r, Genoma parent1, Genoma parent2)
{
Matrix[] SonW = new Matrix[parent1.W.Length];
for (int layer = 0; layer < parent1.W.Length; layer++)
{
double[, ] w = new double[parent1.W[layer].X, parent1.W[layer].Y];
Matrix.MatrixLoop((i, j) => {
if (r.NextDouble() > 0.5)
{
w[i, j] = parent1.W[layer].GetValue(i, j);
}
else
{
w[i, j] = parent2.W[layer].GetValue(i, j);
}
}, parent1.W[layer].X, parent1.W[layer].Y);
SonW[layer] = w;
}
return(new Genoma(SonW));
}
public Perceptron(Genoma genoma, ActivationFunction activationFunction)
{
this.activationFunction = activationFunction;
W = genoma.W;
}
private Individual[] CrossPopulation()
{
var tempPopulation = (Individual[])population.Clone();
int populationSize = tempPopulation.Length;
List <int> crosspop = new List <int> ();
for (int i = 0; i < populationSize; i++)
{
for (int j = 0; j < populationSize - i; j++)
{
for (int k = 0; k < elitismFactor; k++)
{
crosspop.Add(i);
}
}
}
List <Individual> newpop = new List <Individual> ();
for (int i = 0; i < elitism; i++)
{
newpop.Add(tempPopulation[i]);
}
for (int i = 0; i < newIndividuals; i++)
{
newpop.Add(GenerateIndividual());
}
if (sexualMultiplication)
{
for (int i = 0; i < populationSize - newIndividuals - elitism; i++)
{
var p1 = tempPopulation[crosspop[Random.Range(0, crosspop.Count)]];
var p2 = tempPopulation[crosspop[Random.Range(0, crosspop.Count)]];
Individual individual = new Individual {
gen = Genoma.Cross(
r,
p1.gen,
p2.gen
),
fitness = 0
};
newpop.Add(individual);
}
}
else
{
for (int i = 0; i < populationSize - newIndividuals - elitism; i++)
{
var p1 = tempPopulation[crosspop[Random.Range(0, crosspop.Count)]];
Individual individual = new Individual()
{
gen = new Genoma(p1.gen.W),
fitness = 0
};
newpop.Add(individual);
}
}
return(newpop.ToArray());
}
