// initialize sigma
final double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
sigmaArray[i][0] = inputSigma[i];
}
final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = FastMath.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1 / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4 + mueff / dimension) /
(dimension + 4 + 2 * mueff / dimension);
cs = (mueff + 2) / (dimension + mueff + 3.);
damps = (1 + 2 * FastMath.max(0, FastMath.sqrt((mueff - 1) /
(dimension + 1)) - 1)) *
FastMath.max(0.3,
1 - dimension / (1e-6 + maxIterations)) + cs; // minor increment
ccov1 = 2 / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = FastMath.min(1 - ccov1, 2 * (mueff - 2 + 1 / mueff) /
((dimension + 2) * (dimension + 2) + mueff));
ccov1Sep = FastMath.min(1, ccov1 * (dimension + 1.5) / 3);
ccovmuSep = FastMath.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3);
chiN = FastMath.sqrt(dimension) *
(1 - 1 / ((double) 4 * dimension) + 1 / ((double) 21 * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective variables
diagD = insigma.scalarMultiply(1 / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();