public static IIndividual CrossOver(List <IIndividual> parents, Random random)
{
var newInd = new GotchaIndividual();
for (var ii = 0; ii < 4; ++ii)
{
for (var jj = 0; jj < 4; ++jj)
{
//33% chance of taking single random parent gene, 66% chance of taking average of all parents
var dice = random.Next(0, 3);
if (dice < 1)
{
//Take average of parents respective alleles
var tot = 0.0f;
for (var kk = 0; kk < parents.Count; ++kk)
{
var par = (GotchaIndividual)parents[kk];
tot += par.mGradient[ii][jj];
}
tot = tot / parents.Count;
newInd.mGradient[ii][jj] = tot;
}
else //Take single parent gene randomly
{
newInd.mGradient[ii][jj] = ((GotchaIndividual)parents[random.Next(0, parents.Count)]).mGradient[ii][jj];
}
}
}
return(newInd);
}
public GotchaIndividual(GotchaIndividual other)
{
mGradient = new float[4][];
for (var ii = 0; ii < 4; ++ii)
{
mGradient[ii] = new float[4];
for (var jj = 0; jj < 4; ++jj)
{
mGradient[ii][jj] = other.mGradient[ii][jj];
}
}
mName = IndividualTools.CreateName();
}
public IIndividual Mutate(float probability, float sigma, Random rng)
{
var newInd = new GotchaIndividual(this);
for (var ii = 0; ii < 4; ++ii)
{
for (var jj = 0; jj < 4; ++jj)
{
if (rng.ProbabilityPass(probability))
{
newInd.mGradient[ii][jj] = (float)(mGradient[ii][jj] + rng.GausianNoise(sigma));
}
}
}
return(newInd);
}
