private double[] mutate(CMAParamSet params, double[] x, double[][] range, int count) {
int dim = range.length;
if (!Mathematics.isInRange(params.meanX, range)) { // TODO Im not sure why this can happen...
System.err.println("Error in MutateESRankMuCMA.mutate !");
Mathematics.projectToRange(params.meanX, range);
}
// System.out.println("--- In mutate!");
if (params != null && (params.firstAdaptionDone)) {
double[] sampl = new double[dim]; // generate scaled random vector (D * z)
for (int i = 0; i < dim; ++i) {
sampl[i] = Math.sqrt(params.eigenvalues[i]) * RNG.gaussianDouble(1.);
if (Double.isNaN(sampl[i])) sampl[i] = 0;
}
// System.out.println("Sampling around " + BeanInspector.toString(meanX));
/* add mutation (sigma * B * (D*z)) */
addMutationStep(params, x, sampl);
int cnt = 0;
while (!Mathematics.isInRange(x, range)) {
// % You may handle constraints here. You may either resample
// % arz(:,k) and/or multiply it with a factor between -1 and 1
// % (the latter will decrease the overall step size) and
// % recalculate arx accordingly. Do not change arx or arz in any
// % other way.
// multiply sampl by a no in [-1,1] and try again
double r; // actually we use [-1,-0.5] or [0.5, 1]
cnt++;
if (cnt > 10 * x.length) {
// well... lets give it up. Probably the meanX is close to the bounds in several
// dimensions
// which is unlikely to be solved by random sampling.
// if (!Mathematics.isInRange(params.meanX, range)) {
// System.err.println("Error in MutateESRankMuCMA.mutate !"); break;
// }
r = 0;
} else {
r = RNG.randomDouble(-0.5, 0.5);
if (Math.abs(r) < 0.5) r += Math.signum(r) * 0.5; // r is in [-1,-0.5] or [0.5,1]
}
// System.out.println("Reducing step by " + r + " for " +
// BeanInspector.toString(params.meanX));
Mathematics.svMult(r, sampl, sampl);
addMutationStep(params, x, sampl);
}
} else {
if (params == null) {
System.err.println(
"Error in MutateESRankMuCMA: parameter set was null! Skipping mutation...");
} // no valid meanX yet, so just do a gaussian jump with sigma
for (int i = 0; i < dim; ++i) {
x[i] += RNG.gaussianDouble(getSigma(params, i));
checkValidDouble(x[i]);
}
}
if (!Mathematics.isInRange(params.meanX, range)) {
System.err.println("Error B in MutateESRankMuCMA.mutate !");
}
if (TRACE_1) System.out.println("mutated indy: " + BeanInspector.toString(x));
return x;
}
/**
* To be called from Matlab giving the result of the question.
*
* @param y
*/
public void setAnswer(double[] y) {
// logMPAndSysOut("Synch requesting B setAnswer " + getState());
synchronized (requesting) {
// logMPAndSysOut("In Synch requesting B setAnswer " + getState());
if (!requesting) {
String msg = "Error: not in requesting state when answer arrived!!";
System.err.println(msg);
logMP(msg);
}
// System.err.println("answer is " + BeanInspector.toString(y));
if (y == null) {
System.err.println("Error: Matlab function returned null array - this is bad.");
System.err.println("X-value was " + BeanInspector.toString(getQuestion()));
}
answer = y;
requesting = false; // answer is finished, break request loop
logMP("-- setAnswer: " + BeanInspector.toString(y) + ", req state is " + requesting + "\n");
}
// logMPAndSysOut("Synch requesting B done " + getState());
}
public String getState() {
return "ID: "
+ runID
+ ", qu: "
+ BeanInspector.toString(question)
+ " quit,fin,req "
+ quit
+ ","
+ fin
+ ","
+ requesting;
}
/**
* 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));
}
double[] getAnswer() {
logMP("-- mediator delivering " + BeanInspector.toString(answer) + "\n");
return answer;
}
/**
* To be called from Matlab.
*
* @return
*/
public Object getQuestion() {
logMP("-- Question: " + BeanInspector.toString(question) + "\n");
return question;
}