/**
* Requires selected population to be sorted by fitness.
*
* @param params refering parameter set
* @param iterations number of iterations performed
* @param selected selected population
* @return true if the parameters seem ok or were corrected, false if new parameters must be
* produced
*/
private boolean testAndCorrectNumerics(
CMAParamSet params, int iterations, Population selected) { // not much left here
boolean corrected = true;
/* Flat Fitness, Test if function values are identical */
if (iterations > 1 && (selected.size() > 1)) {
// selected pop is sorted
if (nearlySame(
selected.getEAIndividual(0).getFitness(),
selected.getEAIndividual(selected.size() - 1).getFitness())) {
if (TRACE_1)
System.err.println(
"flat fitness landscape, consider reformulation of fitness, step-size increased");
params.sigma *= Math.exp(0.2 + params.c_sig / params.d_sig);
// sigma=0.1;
}
}
if (!checkValidDouble(params.sigma)) {
System.err.println("Error, unstable sigma!");
corrected = false;
// params.sigma=params.firstSigma; // MK TODO
// System.err.println(
}
/* Align (renormalize) scale C (and consequently sigma) */
/* e.g. for infinite stationary state simulations (noise
* handling needs to be introduced for that) */
double fac = 1.;
double minEig = 1e-12;
double maxEig = 1e8;
if (Mathematics.max(params.eigenvalues) < minEig)
fac = 1. / Math.sqrt(Mathematics.max(params.eigenvalues));
else if (Mathematics.min(params.eigenvalues) > maxEig)
fac = 1. / Math.sqrt(Mathematics.min(params.eigenvalues));
if (fac != 1.) {
// System.err.println("Scaling by " + fac);
params.sigma /= fac;
for (int i = 0; i < params.meanX.length; ++i) {
params.pathC[i] *= fac;
params.eigenvalues[i] *= fac * fac;
for (int j = 0; j <= i; ++j) {
params.mC.set(i, j, params.mC.get(i, j) * fac * fac);
if (i != j) params.mC.set(j, i, params.mC.get(i, j));
}
}
}
return corrected;
} // Test...
private void plotAll(Population pop) {
// TODO
double pos[], vel[];
for (int i = 0; i < pop.size(); i++) {
pos = ((TribesExplorer) pop.getEAIndividual(i)).getDoubleData();
vel = ((TribesExplorer) pop.getEAIndividual(i)).getVelocity();
plotIndy(pos, vel, i);
// hier weiter!
}
}
/* update C */
private void updateCov(
CMAParamSet params,
double[] newPathC,
double[] newMeanX,
double hsig,
int mu,
Population selected) {
double newVal = 0;
int dim = newMeanX.length;
double ccv = getCCov(params.weights, mu, dim);
if (ccv > 0) {
double mcv = CMAParamSet.getMuCov(params.weights, mu);
/* (only upper triangle!) */
/* update covariance matrix */
// System.out.println("CCov " + getCCov(selected) + " Cc " + getCc() + " muCov " +
// getMuCov(selected));
for (int i = 0; i < dim; ++i)
for (int j = 0; j <= i; ++j) {
// oldVal = mC.get(i,j);
newVal =
(1 - ccv) * params.mC.get(i, j)
+ ccv
* (1. / mcv)
* (newPathC[i] * newPathC[j]
+ (1 - hsig) * getCc() * (2. - getCc()) * params.mC.get(i, j));
checkValidDouble(newVal);
params.mC.set(i, j, newVal);
if (isRankMu()) {
for (int k = 0; k < mu; ++k) {
/*
* additional rank mu
* update
*/
// double[] x_k =
// ((InterfaceDataTypeDouble)selected.getEAIndividual(k)).getDoubleData();
double[] x_k =
AbstractEAIndividual.getDoublePositionShallow(selected.getEAIndividual(k));
newVal =
params.mC.get(i, j)
+ ccv
* (1 - 1. / mcv)
* params.weights[k]
* (x_k[i] - params.meanX[i])
* (x_k[j] - params.meanX[j])
/ (getSigma(params, i) * getSigma(params, j)); // TODO right sigmas?
checkValidDouble(newVal);
params.mC.set(i, j, newVal);
}
}
}
// fill rest of C
for (int i = 0; i < dim; ++i) {
for (int j = i + 1; j < dim; ++j) {
params.mC.set(i, j, params.mC.get(j, i));
}
}
if (params.mC.get(0, 1) != params.mC.get(1, 0)) {
System.err.println("WARNING, C is not symmetric!");
}
// maxsqrtdiagC = Math.sqrt(math.max(math.diag(C)));
// minsqrtdiagC = Math.sqrt(math.min(math.diag(C)));
} // update of C
}
/**
* Perform the main adaption of sigma and C using evolution paths. The evolution path is deduced
* from the center of the selected population compared to the old mean value. See Hansen&Kern 04
* for further information.
*
* @param oldGen
* @param selectedP
*/
public void adaptAfterSelection(Population oldGen, Population selectedP) {
Population selectedSorted =
selectedP.getSortedBestFirst(new AbstractEAIndividualComparator(-1));
int mu, lambda;
mu = selectedP.size();
lambda = oldGen.size();
int generation = oldGen.getGeneration();
if (mu >= lambda) {
// try to override by oldGen additional data:
if (oldGen.hasData(EvolutionStrategies.esMuParam))
mu = (Integer) oldGen.getData(EvolutionStrategies.esMuParam);
if (oldGen.hasData(EvolutionStrategies.esLambdaParam))
lambda = (Integer) oldGen.getData(EvolutionStrategies.esLambdaParam);
}
if (mu >= lambda) {
mu = Math.max(1, lambda / 2);
EVAERROR.errorMsgOnce(
"Warning: invalid mu/lambda ratio! Setting mu to lambda/2 = "
+ mu
+ ", lambda = "
+ lambda);
}
CMAParamSet params;
if (oldGen.getGeneration()
<= 1) { // init new param set. At gen < 1 we shouldnt be called, but better do it once too
// often
if (oldGen.hasData(cmaParamsKey))
params =
CMAParamSet.initCMAParams(
(CMAParamSet) oldGen.getData(cmaParamsKey),
mu,
lambda,
oldGen,
getInitSigma(oldGen));
else params = CMAParamSet.initCMAParams(mu, lambda, oldGen, getInitSigma(oldGen));
} else {
if (!oldGen.hasData(cmaParamsKey)) {
if (oldGen.getGeneration() > 1)
EVAERROR.errorMsgOnce("Error: population lost cma parameters. Incompatible optimizer?");
params = CMAParamSet.initCMAParams(mu, lambda, oldGen, getInitSigma(oldGen));
} else params = (CMAParamSet) oldGen.getData(cmaParamsKey);
}
if (lambda == 1
&& (oldGen.size() == 1)
&& (selectedP.size() == 1)
&& (oldGen.getEAIndividual(0).equals(selectedP.getEAIndividual(0)))) {
// nothing really happened, so do not adapt and just store default params
lastParams = (CMAParamSet) params.clone();
oldGen.putData(cmaParamsKey, params);
selectedP.putData(cmaParamsKey, params);
return;
}
if (TRACE_1) {
System.out.println("WCMA adaptGenerational **********");
// System.out.println("newPop measures: " +
// BeanInspector.toString(newPop.getPopulationMeasures()));
System.out.println("mu_eff: " + CMAParamSet.getMuEff(params.weights, mu));
System.out.println(params.toString());
System.out.println("*********************************");
}
double[] newMeanX = calcMeanX(params.weights, selectedSorted);
if (TRACE_1) System.out.println("newMeanX: " + BeanInspector.toString(newMeanX));
int dim = params.meanX.length;
double[] BDz = new double[dim];
for (int i = 0; i < dim; i++) {
/* calculate xmean and BDz~N(0,C) */
// Eq. 4 from HK04, most right term
BDz[i] =
Math.sqrt(CMAParamSet.getMuEff(params.weights, mu))
* (newMeanX[i] - params.meanX[i])
/ getSigma(params, i);
}
// if (TRACE_2) System.out.println("BDz is " + BeanInspector.toString(BDz));
double[] newPathS = params.pathS.clone();
double[] newPathC = params.pathC.clone();
double[] zVect = new double[dim];
/* calculate z := D^(-1) * B^(-1) * BDz into artmp, we could have stored z instead */
for (int i = 0; i < dim; ++i) {
double sum = 0.;
for (int j = 0; j < dim; ++j) {
sum += params.mB.get(j, i) * BDz[j]; // times B transposed, (Eq 4) in HK04
}
if (params.eigenvalues[i] < 0) {
EVAERROR.errorMsgOnce(
"Warning: negative eigenvalue in MutateESRankMuCMA! (possibly multiple cases)");
zVect[i] = 0;
} else {
zVect[i] = sum / Math.sqrt(params.eigenvalues[i]);
if (!checkValidDouble(zVect[i])) {
System.err.println("Error, infinite zVect entry!");
zVect[i] = 0; // TODO MK
}
}
}
/* cumulation for sigma (ps) using B*z */
for (int i = 0; i < dim; ++i) {
double sum = 0.;
for (int j = 0; j < dim; ++j) sum += params.mB.get(i, j) * zVect[j];
newPathS[i] =
(1. - params.c_sig) * params.pathS[i]
+ Math.sqrt(params.c_sig * (2. - params.c_sig)) * sum;
if (!checkValidDouble(newPathS[i])) {
System.err.println("Error, infinite pathS!");
}
}
// System.out.println("pathS diff: " + BeanInspector.toString(Mathematics.vvSub(newPathS,
// pathS)));
// System.out.println("newPathS is " + BeanInspector.toString(newPathS));
double psNorm = Mathematics.norm(newPathS);
double hsig = 0;
if (psNorm / Math.sqrt(1. - Math.pow(1. - params.c_sig, 2. * generation)) / expRandStepLen
< 1.4 + 2. / (dim + 1.)) {
hsig = 1;
}
for (int i = 0; i < dim; ++i) {
newPathC[i] =
(1. - getCc()) * params.pathC[i] + hsig * Math.sqrt(getCc() * (2. - getCc())) * BDz[i];
checkValidDouble(newPathC[i]);
}
// TODO missing: "remove momentum in ps"
if (TRACE_1) {
System.out.println("newPathC: " + BeanInspector.toString(newPathC));
System.out.println("newPathS: " + BeanInspector.toString(newPathS));
}
if (TRACE_1) System.out.println("Bef: C is \n" + params.mC.toString());
if (params.meanX == null) params.meanX = newMeanX;
updateCov(params, newPathC, newMeanX, hsig, mu, selectedSorted);
updateBD(params);
if (TRACE_2) System.out.println("Aft: C is " + params.mC.toString());
/* update of sigma */
double sigFact = Math.exp(((psNorm / expRandStepLen) - 1) * params.c_sig / params.d_sig);
if (Double.isInfinite(sigFact))
params.sigma *= 10.; // in larger search spaces sigma tends to explode after init.
else params.sigma *= sigFact;
if (!testAndCorrectNumerics(params, generation, selectedSorted)) {
// parameter seemingly exploded...
params =
CMAParamSet.initCMAParams(
params,
mu,
lambda,
params.meanX,
((InterfaceDataTypeDouble) oldGen.getEAIndividual(0)).getDoubleRange(),
params.firstSigma);
}
if (TRACE_1) {
System.out.println("sigma=" + params.sigma);
System.out.print("psLen=" + (psNorm) + " ");
outputParams(params, mu);
}
// take over data
params.meanX = newMeanX;
params.pathC = newPathC;
params.pathS = newPathS;
params.firstAdaptionDone = true;
lastParams = (CMAParamSet) params.clone();
oldGen.putData(cmaParamsKey, params);
selectedP.putData(cmaParamsKey, params);
// if (TRACE_2) System.out.println("sampling around " + BeanInspector.toString(meanX));
}