public Individual(Individual other)
{
SuccessProbability = other.SuccessProbability;
sigma = other.sigma;
evolutionPath = (RealVector)other.evolutionPath.Clone();
Mean = (RealVector)other.Mean.Clone();
lowerCholesky = (double[, ])other.lowerCholesky.Clone();
Selected = true;
strategy = other.strategy;
}
public Individual(RealVector mean, double pSucc, double sigma, RealVector pc, double[,] c, MOCMAEvolutionStrategy strategy)
{
Mean = mean;
lastStep = new RealVector(mean.Length);
SuccessProbability = pSucc;
this.sigma = sigma;
evolutionPath = pc;
CholeskyDecomposition(c);
Selected = true;
this.strategy = strategy;
}
protected MOCMAEvolutionStrategy(MOCMAEvolutionStrategy original, Cloner cloner) : base(original, cloner)
{
random = cloner.Clone(original.random);
gauss = cloner.Clone(original.gauss);
solutions = original.solutions != null?original.solutions.Select(cloner.Clone).ToArray() : null;
stepSizeLearningRate = original.stepSizeLearningRate;
stepSizeDampeningFactor = original.stepSizeDampeningFactor;
targetSuccessProbability = original.targetSuccessProbability;
evolutionPathLearningRate = original.evolutionPathLearningRate;
covarianceMatrixLearningRate = original.covarianceMatrixLearningRate;
covarianceMatrixUnlearningRate = original.covarianceMatrixUnlearningRate;
successThreshold = original.successThreshold;
}
public Individual(Individual other, Cloner cloner)
{
strategy = cloner.Clone(other.strategy);
Mean = cloner.Clone(other.Mean);
sigma = other.sigma;
evolutionPath = cloner.Clone(other.evolutionPath);
lastStep = cloner.Clone(other.evolutionPath);
lastZ = cloner.Clone(other.lastZ);
lowerCholesky = other.lowerCholesky != null?other.lowerCholesky.Clone() as double[, ] : null;
Fitness = other.Fitness != null?other.Fitness.Select(x => x).ToArray() : null;
PenalizedFitness = other.PenalizedFitness != null?other.PenalizedFitness.Select(x => x).ToArray() : null;
Selected = other.Selected;
SuccessProbability = other.SuccessProbability;
}
