public void Crossover(VRandom rng, NetworkComp genome)
{
//determins the rate of crossover
double rate = rng.RandDouble(0.25, 0.75);
//calculates the maximum number of nurons and axons
//this is nessary incase the genomes have diffrent numbers
int nmax = Math.Min(this.NuronCount, genome.NuronCount);
int amax = Math.Min(this.AxonCount, genome.AxonCount);
for (int i = 0; i < nmax; i++)
{
if (rng.NextDouble() > rate)
{
continue;
}
NuronComp copy = genome.nurons[i];
nurons[i].Funciton = copy.Funciton;
}
for (int i = 0; i < amax; i++)
{
if (rng.NextDouble() > rate)
{
continue;
}
//NOTE: should we copy the entier axon or just the weight?
AxonComp copy = genome.axons[i];
axons[i].Weight = copy.Weight;
axons[i].Enabled = copy.Enabled;
axons[i].Input = copy.Input;
axons[i].Output = copy.Output;
}
throw new NotImplementedException();
}
//In this method we assume a standard mutation rate of 1.0;
public void Mutate(VRandom rng, double rate)
{
//makes shure the rate is positive
rate = Math.Abs(rate);
double selector = rng.NextDouble();
if (selector < P_EXPAND)
{
//expands the network
Expand(rng);
}
else if (selector < P_TOGGEL)
{
Axon ax = GetRandomAxon(rng);
if (ax.Enabled)
{
//outright disables the nuron
ax.Enabled = false;
}
else
{
//resets the neuron to a large weight
ax.Weight = rng.RandGauss() * SD_NEW;
ax.Enabled = true;
}
}
else
{
Axon ax = GetRandomAxon(rng);
if (ax.Enabled)
{
//permutes the weight by a small amount
double delta = rng.RandGauss() * SD_SHIFT;
ax.Weight = ax.Weight + delta;
}
else
{
//resets the nuron to a small weight
double delta = rng.RandGauss() * SD_SHIFT;
ax.Weight = delta;
ax.Enabled = true;
}
}
}
/// <summary>
/// Combines the genes of the curent nural net with the genes of
/// another network to create a brand new offspring. The idea is
/// that the child network will possess trates from both its
/// parents, similar to sexual reproduction.
/// </summary>
/// <param name="rng">Random number generator</param>
/// <param name="mate">Mate of the curent network</param>
/// <returns>The child of both networks</returns>
public NetworkCPP Combine(VRandom rng, NetworkCPP mate)
{
//makes a clone of the dominate parent
NetworkCPP child = new NetworkCPP(rng, this);
//determins weather or not to do liniar crossover
bool liniar = rng.RandBool(P_Linear);
double a = rng.NextDouble();
//lists all the axons in the mate
var axons = mate.ListAxons();
foreach (Axon ax in axons)
{
//obtains the matching child axon
Axon axc = child.FindMatch(ax);
if (axc == null)
{
continue;
}
if (liniar)
{
//chooses a value between the two weights
double weight = axc.Weight * (1.0 - a);
axc.Weight = weight + (ax.Weight * a);
}
else
{
//determins the new weight based on crossover
bool cross = rng.RandBool();
if (cross)
{
axc.Weight = ax.Weight;
}
}
//has a chance of enabling if either are disabled
bool en = ax.Enabled && axc.Enabled;
axc.Enabled = en || rng.RandBool(0.25);
}
return(child);
}
public void Crossover(VRandom rng, NetworkAuto genome)
{
//throw new NotImplementedException();
//determins weather or not to do liniar crossover
bool liniar = rng.RandBool(P_Linear);
double a = rng.NextDouble();
//lists all the axons and nurons in the mate
var ittr1 = genome.nurons.ListItems();
var ittr2 = genome.axons.ListItems();
foreach (Nuron n in ittr1)
{
//obtains the matching child nuron
Nuron nc = nurons.GetValue(n.Index);
if (nc == null)
{
//adds the missing nuron
nc = new Nuron(this, n);
nurons.Add(nc.Index, nc);
}
//else
//{
// //determins the activation based on crossover
// bool cross = rng.RandBool();
// if (cross) nc.Func = n.Func;
//}
}
foreach (Axon ax in ittr2)
{
//obtains the matching child axon
Axon axc = axons.GetValue(ax.Index);
if (axc == null)
{
//adds the missing axon, but disabled
axc = new Axon(ax);
axc.Enabled = false;
axons.Add(axc.Index, axc);
}
else
{
if (liniar)
{
//chooses a value between the two weights
double weight = axc.Weight * (1.0 - a);
axc.Weight = weight + (ax.Weight * a);
}
else
{
//determins the new weight based on crossover
bool cross = rng.RandBool();
if (cross)
{
axc.Weight = ax.Weight;
}
}
//if the axon is present in both networks, it has a
//strong chance of becoming enabled
bool en = ax.Enabled && axc.Enabled;
axc.Enabled = en || rng.RandBool(0.25);
}
}
}
