private CMAParameters(CMAParameters original, Cloner cloner)
: base(original, cloner) {
this.AxisRatio = original.AxisRatio;
if (original.B != null) this.B = (double[,])original.B.Clone();
if (original.BDz != null) this.BDz = (double[])original.BDz.Clone();
if (original.C != null) this.C = (double[,])original.C.Clone();
this.CCov = original.CCov;
this.CCovSep = original.CCovSep;
this.CC = original.CC;
this.ChiN = original.ChiN;
this.CS = original.CS;
if (original.D != null) this.D = (double[])original.D.Clone();
this.Damps = original.Damps;
this.InitialIterations = original.InitialIterations;
this.Mu = original.Mu;
this.MuCov = original.MuCov;
this.MuEff = original.MuEff;
if (original.PC != null) this.PC = (double[])original.PC.Clone();
if (original.PS != null) this.PS = (double[])original.PS.Clone();
this.Sigma = original.Sigma;
if (original.SigmaBounds != null) this.SigmaBounds = (double[,])original.SigmaBounds.Clone();
if (original.Weights != null) this.Weights = (double[])original.Weights.Clone();
if (original.QualityHistory != null) this.QualityHistory = new Queue<double>(original.QualityHistory);
this.QualityHistorySize = original.QualityHistorySize;
}
private CMAParameters(CMAParameters original, Cloner cloner)
: base(original, cloner)
{
this.AxisRatio = original.AxisRatio;
if (original.B != null)
{
this.B = (double[, ])original.B.Clone();
}
if (original.BDz != null)
{
this.BDz = (double[])original.BDz.Clone();
}
if (original.C != null)
{
this.C = (double[, ])original.C.Clone();
}
this.CCov = original.CCov;
this.CCovSep = original.CCovSep;
this.CC = original.CC;
this.ChiN = original.ChiN;
this.CS = original.CS;
if (original.D != null)
{
this.D = (double[])original.D.Clone();
}
this.Damps = original.Damps;
this.InitialIterations = original.InitialIterations;
this.Mu = original.Mu;
this.MuCov = original.MuCov;
this.MuEff = original.MuEff;
if (original.PC != null)
{
this.PC = (double[])original.PC.Clone();
}
if (original.PS != null)
{
this.PS = (double[])original.PS.Clone();
}
this.Sigma = original.Sigma;
if (original.SigmaBounds != null)
{
this.SigmaBounds = (double[, ])original.SigmaBounds.Clone();
}
if (original.Weights != null)
{
this.Weights = (double[])original.Weights.Clone();
}
if (original.QualityHistory != null)
{
this.QualityHistory = new Queue <double>(original.QualityHistory);
}
this.QualityHistorySize = original.QualityHistorySize;
}
public override IOperation Apply() {
var N = DimensionParameter.ActualValue.Value;
var lambda = PopulationSizeParameter.ActualValue.Value;
var mu = MuParameter.ActualValue;
var sp = new CMAParameters();
sp.Mu = mu == null ? (int)Math.Floor(lambda / 2.0) : mu.Value;
sp.QualityHistorySize = 10 + 30 * N / lambda;
sp.QualityHistory = new Queue<double>(sp.QualityHistorySize + 1);
var s = InitialSigmaParameter.ActualValue;
if (s == null || s.Length == 0) throw new InvalidOperationException("Initial standard deviation (sigma) must be given.");
var sigma = s.Max();
if (sigma <= 0) throw new InvalidOperationException("Initial standard deviation (sigma) must be > 0.");
var pc = new double[N]; // evolution paths for C
var ps = new double[N]; // evolution paths for sigma
var B = new double[N, N]; // B defines the coordinate system
var D = new double[N]; // diagonal D defines the scaling
var C = new double[N, N]; // covariance matrix C
var BDz = new double[N];
double minSqrtdiagC = int.MaxValue, maxSqrtdiagC = int.MinValue;
for (int i = 0; i < N; i++) {
B[i, i] = 1;
if (s.Length == 1) D[i] = 1;
else if (s.Length == N) D[i] = s[i] / sigma;
else throw new InvalidOperationException("Initial standard deviation (sigma) must either contain only one value for all dimension or for every dimension.");
if (D[i] <= 0) throw new InvalidOperationException("Initial standard deviation (sigma) values must all be > 0.");
C[i, i] = D[i] * D[i];
if (Math.Sqrt(C[i, i]) < minSqrtdiagC) minSqrtdiagC = Math.Sqrt(C[i, i]);
if (Math.Sqrt(C[i, i]) > maxSqrtdiagC) maxSqrtdiagC = Math.Sqrt(C[i, i]);
}
// ensure maximal and minimal standard deviations
var sigmaBounds = SigmaBoundsParameter.ActualValue;
if (sigmaBounds != null && sigmaBounds.Rows > 0) {
for (int i = 0; i < N; i++) {
var d = sigmaBounds[Math.Min(i, sigmaBounds.Rows - 1), 0];
if (d > sigma * minSqrtdiagC) sigma = d / minSqrtdiagC;
}
for (int i = 0; i < N; i++) {
var d = sigmaBounds[Math.Min(i, sigmaBounds.Rows - 1), 1];
if (d > sigma * maxSqrtdiagC) sigma = d / maxSqrtdiagC;
}
}
// end ensure ...
// testAndCorrectNumerics
double fac = 1;
if (D.Max() < 1e-6)
fac = 1.0 / D.Max();
else if (D.Min() > 1e4)
fac = 1.0 / D.Min();
if (fac != 1.0) {
sigma /= fac;
for (int i = 0; i < N; i++) {
pc[i] *= fac;
D[i] *= fac;
for (int j = 0; j < N; j++)
C[i, j] *= fac * fac;
}
}
// end testAndCorrectNumerics
var initialIterations = InitialIterationsParameter.ActualValue;
if (initialIterations == null) {
initialIterations = new IntValue(0);
}
double maxD = D.Max(), minD = D.Min();
if (minD == 0) sp.AxisRatio = double.PositiveInfinity;
else sp.AxisRatio = maxD / minD;
sp.PC = pc;
sp.PS = ps;
sp.B = B;
sp.D = D;
sp.C = C;
sp.BDz = BDz;
sp.Sigma = sigma;
if (sigmaBounds != null) {
sp.SigmaBounds = new double[sigmaBounds.Rows, sigmaBounds.Columns];
for (int i = 0; i < sigmaBounds.Rows; i++)
for (int j = 0; j < sigmaBounds.Columns; j++)
sp.SigmaBounds[i, j] = sigmaBounds[i, j];
}
sp.InitialIterations = initialIterations.Value;
StrategyParametersParameter.ActualValue = sp;
return base.Apply();
}
public override IOperation Apply()
{
var N = DimensionParameter.ActualValue.Value;
var lambda = PopulationSizeParameter.ActualValue.Value;
var mu = MuParameter.ActualValue;
var sp = new CMAParameters();
sp.Mu = mu == null ? (int)Math.Floor(lambda / 2.0) : mu.Value;
sp.QualityHistorySize = 10 + 30 * N / lambda;
sp.QualityHistory = new Queue <double>(sp.QualityHistorySize + 1);
var s = InitialSigmaParameter.ActualValue;
if (s == null || s.Length == 0)
{
throw new InvalidOperationException("Initial standard deviation (sigma) must be given.");
}
var sigma = s.Max();
if (sigma <= 0)
{
throw new InvalidOperationException("Initial standard deviation (sigma) must be > 0.");
}
var pc = new double[N]; // evolution paths for C
var ps = new double[N]; // evolution paths for sigma
var B = new double[N, N]; // B defines the coordinate system
var D = new double[N]; // diagonal D defines the scaling
var C = new double[N, N]; // covariance matrix C
var BDz = new double[N];
double minSqrtdiagC = int.MaxValue, maxSqrtdiagC = int.MinValue;
for (int i = 0; i < N; i++)
{
B[i, i] = 1;
if (s.Length == 1)
{
D[i] = 1;
}
else if (s.Length == N)
{
D[i] = s[i] / sigma;
}
else
{
throw new InvalidOperationException("Initial standard deviation (sigma) must either contain only one value for all dimension or for every dimension.");
}
if (D[i] <= 0)
{
throw new InvalidOperationException("Initial standard deviation (sigma) values must all be > 0.");
}
C[i, i] = D[i] * D[i];
if (Math.Sqrt(C[i, i]) < minSqrtdiagC)
{
minSqrtdiagC = Math.Sqrt(C[i, i]);
}
if (Math.Sqrt(C[i, i]) > maxSqrtdiagC)
{
maxSqrtdiagC = Math.Sqrt(C[i, i]);
}
}
// ensure maximal and minimal standard deviations
var sigmaBounds = SigmaBoundsParameter.ActualValue;
if (sigmaBounds != null && sigmaBounds.Rows > 0)
{
for (int i = 0; i < N; i++)
{
var d = sigmaBounds[Math.Min(i, sigmaBounds.Rows - 1), 0];
if (d > sigma * minSqrtdiagC)
{
sigma = d / minSqrtdiagC;
}
}
for (int i = 0; i < N; i++)
{
var d = sigmaBounds[Math.Min(i, sigmaBounds.Rows - 1), 1];
if (d > sigma * maxSqrtdiagC)
{
sigma = d / maxSqrtdiagC;
}
}
}
// end ensure ...
// testAndCorrectNumerics
double fac = 1;
if (D.Max() < 1e-6)
{
fac = 1.0 / D.Max();
}
else if (D.Min() > 1e4)
{
fac = 1.0 / D.Min();
}
if (fac != 1.0)
{
sigma /= fac;
for (int i = 0; i < N; i++)
{
pc[i] *= fac;
D[i] *= fac;
for (int j = 0; j < N; j++)
{
C[i, j] *= fac * fac;
}
}
}
// end testAndCorrectNumerics
var initialIterations = InitialIterationsParameter.ActualValue;
if (initialIterations == null)
{
initialIterations = new IntValue(0);
}
double maxD = D.Max(), minD = D.Min();
if (minD == 0)
{
sp.AxisRatio = double.PositiveInfinity;
}
else
{
sp.AxisRatio = maxD / minD;
}
sp.PC = pc;
sp.PS = ps;
sp.B = B;
sp.D = D;
sp.C = C;
sp.BDz = BDz;
sp.Sigma = sigma;
if (sigmaBounds != null)
{
sp.SigmaBounds = new double[sigmaBounds.Rows, sigmaBounds.Columns];
for (int i = 0; i < sigmaBounds.Rows; i++)
{
for (int j = 0; j < sigmaBounds.Columns; j++)
{
sp.SigmaBounds[i, j] = sigmaBounds[i, j];
}
}
}
sp.InitialIterations = initialIterations.Value;
StrategyParametersParameter.ActualValue = sp;
return(base.Apply());
}
