/** Revises the CMA-ES distribution to reflect the current fitness results in the provided subpopulation. */
public void UpdateDistribution(IEvolutionState state, Subpopulation subpop)
{
// % Sort by fitness and compute weighted mean into xmean
// [arfitness, arindex] = sort(arfitness); % minimization
// xmean = arx(:,arindex(1:mu))*weights; % recombination % Eq.39
// counteval += lambda;
// only need partial sort?
((List <Individual>)subpop.Individuals).Sort();
SimpleMatrixD artmp = new SimpleMatrixD(GenomeSize, mu);
SimpleMatrixD xold = xmean;
xmean = new SimpleMatrixD(GenomeSize, 1);
for (int i = 0; i < mu; i++)
{
DoubleVectorIndividual dvind = (DoubleVectorIndividual)subpop.Individuals[i];
// won't modify the genome
SimpleMatrixD arz = new SimpleMatrixD(GenomeSize, 1, true, dvind.genome);
arz = (arz.minus(xold).divide(sigma));
for (int j = 0; j < GenomeSize; j++)
{
xmean.set(j, 0, xmean.get(j, 0) + weights[i] * dvind.genome[j]);
artmp.set(j, i, arz.get(j, 0));
}
}
// % Cumulation: Update evolution paths
SimpleMatrixD y = xmean.minus(xold).divide(sigma);
SimpleMatrixD bz = invsqrtC.mult(y);
SimpleMatrixD bz_scaled = bz.scale(Math.Sqrt(cs * (2.0 - cs) * mueff));
ps = ps.scale(1.0 - cs).plus(bz_scaled);
double h_sigma_value =
((ps.dot(ps) / (1.0 - Math.Pow(1.0 - cs, 2.0 * (state.Generation + 1)))) / GenomeSize);
int hsig = (h_sigma_value < (2.0 + (4.0 / (GenomeSize + 1)))) ? 1 : 0;
SimpleMatrixD y_scaled = y.scale(hsig * Math.Sqrt(cc * (2.0 - cc) * mueff));
pc = pc.scale(1.0 - cc).plus(y_scaled);
// % Adapt covariance matrix C
c = c.scale(1.0 - c1 - cmu);
c = c.plus(pc.mult(pc.transpose()).plus(c.scale((1.0 - hsig) * cc * (2.0 - cc))).scale(c1));
c = c.plus((artmp.mult(SimpleMatrixD.diag(weights).mult(artmp.transpose()))).scale(cmu));
// % Adapt step-size sigma
sigma = sigma * Math.Exp((cs / damps) * (ps.normF() / chiN - 1.0));
// % Update B and D from C
if ((state.Generation - lastEigenDecompositionGeneration) > 1.0 / ((c1 + cmu) * GenomeSize * 10.0))
{
lastEigenDecompositionGeneration = state.Generation;
// make sure the matrix is symmetric (it should be already)
// not sure if this is necessary
for (int i = 0; i < GenomeSize; i++)
{
for (int j = 0; j < i; j++)
{
c.set(j, i, c.get(i, j));
}
}
// this copy gets modified by the decomposition
DMatrixRMaj copy = c.copy().getMatrix();
EigenDecomposition <DMatrixRMaj> eig = DecompositionFactory_DDRM.eig(GenomeSize, true, true);
if (eig.decompose(copy))
{
SimpleMatrixD dinv = new SimpleMatrixD(GenomeSize, GenomeSize);
for (int i = 0; i < GenomeSize; i++)
{
double eigrt = Math.Sqrt(eig.getEigenValue(i).real);
d.set(i, i, eigrt);
dinv.set(i, i, 1 / eigrt);
CommonOps_DDRM.insert(eig.getEigenVector(i), b.getMatrix(), 0, i);
}
invsqrtC = b.mult(dinv.mult(b.transpose()));
CommonOps_DDRM.mult(b.getMatrix(), d.getMatrix(), bd);
}
else
{
state.Output.Fatal("CMA-ES eigendecomposition failed. ");
}
}
CommonOps_DDRM.scale(sigma, bd, sbd);
// % Break, if fitness is good enough or condition exceeds 1e14, better termination methods are advisable
// if arfitness(1) <= stopfitness || max(D) > 1e7 * min(D)
// break;
// end
if (useAltTermination && CommonOps_DDRM.elementMax(d.diag().getMatrix()) >
1e7 * CommonOps_DDRM.elementMin(d.diag().getMatrix()))
{
state.Evaluator.SetRunCompleted("CMAESSpecies: Stopped because matrix condition exceeded limit.");
}
}
