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;
}
private double[] mutateOrig(CMAParamSet params, double[] x, double[][] range, int count) {
int dim = range.length;
int maxRetries;
if (checkRange) maxRetries = 100 * dim;
else maxRetries = 0; // take the first sample, not matter in or out of range
do { // this is a loop in case that the range needs to be checked and the current sampling fails
// to keep the range
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)) */
for (int i = 0; i < dim; ++i) {
double sum = 0.;
for (int j = 0; j < dim; ++j) sum += params.mB.get(i, j) * sampl[j];
x[i] = params.meanX[i] + getSigma(params, i) * sum;
checkValidDouble(x[i]);
}
} 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]);
}
}
} while ((maxRetries--) > 0 && !(Mathematics.isInRange(x, range)));
if (checkRange && !(Mathematics.isInRange(x, range)))
return repairMutation(x, range); // allow some nice tries before using brute force
else return x;
}
