public ColumnVector fitnessHistory; // [vector<double>]
/// <summary>
///
/// </summary>
/// <param name="params_"></param>
/// <param name="m"></param>
/// <param name="sigma_"></param>
public CMAState(CMAParams params_, ColumnVector m, double sigma_)
{
p = params_;
C = new SquareMatrix(p.N);
B = new SquareMatrix(p.N);
d = new double[p.N];
pc = new double[p.N];
ps = new double[p.N];
mean = m;
sigma = sigma_;
gen = 0;
fitnessHistory = new ColumnVector(3);
double trace = p.InitialStdDevs.Sum(x => x * x);
/* Initialize covariance structure */
for (int i = 0; i < p.N; ++i)
{
B[i, i] = 1.0;
d[i] = p.InitialStdDevs[i] * Math.Sqrt(p.N / trace);
C[i, i] = d[i] * d[i];
pc[i] = 0.0;
ps[i] = 0.0;
}
}
/// <summary>
/// http://eodev.sourceforge.net/eo/doc/html/_c_m_a_state_8cpp_source.html
/// </summary>
public CmaEs(
Func <double[], object, double> fitnessMinimizationFunction,
double[] initialParameters,
object userData = null,
double[] minimumStandardDeviations = null,
double[] initialStandardDeviations = null,
double stopFitnessMinimization = 1e-10,
int additionalPopulationSize = 0,
int maxRestarts = 100,
double stopEvalsMultiple = 50.0,
double restartPopulationMultiple = 2.0,
double initialVarianceEstimates = 0.5,
double recombinationRatio = 0.5,
double objectiveFunctionTolerance = 1e-12,
double standardDeviationToleranceMultiple = 1e-12)
{
int dimensions = initialParameters.Length;
if (recombinationRatio <= 0)
{
recombinationRatio = 0.5;
}
if (minimumStandardDeviations == null)
{
minimumStandardDeviations = new double[dimensions];
for (int i = 0; i < dimensions; i++)
{
minimumStandardDeviations[i] = DEFAULT_MIN_STDDEV;
}
}
if (initialStandardDeviations == null)
{
initialStandardDeviations = new double[dimensions];
for (int i = 0; i < dimensions; i++)
{
initialStandardDeviations[i] = DEFAULT_INITIAL_STDDEV;
}
}
int lambda = 4 + additionalPopulationSize + (int)(3.0 * Math.Log((double)dimensions));
if (lambda > MAX_POPULATIONSIZE)
{
lambda = MAX_POPULATIONSIZE;
}
int mu = (int)Math.Floor(lambda * recombinationRatio);
this.StopEvals = 100 + (int)(stopEvalsMultiple * Math.Pow(dimensions + 3, 2) / Math.Sqrt(lambda));
this.ObjectiveFunction = fitnessMinimizationFunction;
this.InitialParameters = initialParameters;
this.UserData = userData;
this.StopFitnessMinimization = stopFitnessMinimization;
this.MaxRestarts = maxRestarts;
this.InitialVarianceEstimate = initialVarianceEstimates;
this.RecombinationRatio = recombinationRatio;
this.RestartPopulationMultiple = restartPopulationMultiple;
this.ObjectiveFunctionTolerance = objectiveFunctionTolerance;
this.StandardDeviationToleranceMultiple = standardDeviationToleranceMultiple;
_initialCmaParams = new CMAParams(
dimensions,
MAX_GEN,
lambda,
mu,
minimumStandardDeviations,
initialStandardDeviations,
CMAParams.WeightType.Logarithmic);
}
