Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 123]

▼NEigen
▼Ninternal
Cfunctor_traits< scalar_normal_dist_op< Scalar > >
Cscalar_normal_dist_op
CEigenMultivariateNormal
▼Nlibcmaes	Linear scaling of the parameter space to achieve similar sensitivity across all components
CACMSurrogateStrategy	ACM Surrogate strategy for CMA-ES, follows: 'Surrogate-Assisted Evolutionary Algorithms', Ilya Loshchilov, PhD Thesis, Universite Paris-Sud 11, 2013. http://www.loshchilov.com/phd.html see Chapter 4
CACovarianceUpdate	Active Covariance Matrix update. This implementation closely follows N. Hansen, R. Ros, "Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noiseless Testbed", GECCO'10, 2010
CBIPOPCMAStrategy	Implementation of the BIPOP flavor of CMA-ES, with restarts that control the population of offsprings used in the update of the distribution parameters in order to alternate between local and global searches for the objective
CCandidate	Candidate solution point, in function parameter space
CCMAParameters	Parameters for various flavors of the CMA-ES algorithm
CCMASolutions	Holder of the set of evolving solutions from running an instance of CMA-ES
CCMAStopCriteria	CMA-ES termination criteria, see reference paper in cmastrategy.h
CCMAStrategy	This is an implementation of CMA-ES. It uses the reference algorithm and termination criteria of the following paper: Hansen, N. (2009). Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2389-2395 See https://www.lri.fr/~hansen/publications.html for more information
Ccontour	Function contour as a set of points and values
CCovarianceUpdate	Covariance Matrix update. This is an implementation closely follows: Hansen, N. (2009). Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2389-2395
Cerrstats
CESOptimizer	Optimizer main class
CESOStrategy	Main class describing an evolutionary optimization strategy. Every algorithm in libcmaes descends from this class, and bring its functionalities to an ESOptimizer object
Cfcross	Function crossing as point
CGenoPheno
CIPOPCMAStrategy	Implementation of the IPOP flavor of CMA-ES, with restarts that linearly increase the population of offsprings used in the update of the distribution parameters
ClinScalingStrategy
CNoBoundStrategy
CNoScalingStrategy
COptHopStrategy
CParameters	Generic class for Evolution Strategy parameters
Cpli	Profile likelihood object holder as a set of points and values
CpwqBoundStrategy
CRankedCandidate
CSimpleSurrogateStrategy	Simple surrogate strategy: trains every n steps, and exploits in between, mostly as an example and for testing / debugging surrogates. This strategy overrides the ask/eval/tell functions of the base optimization strategy
CStopCriteria
CSurrogateStrategy	Surrogate base class, to be derived in order to create strategy to be used along with CMA-ES
CVDCMAUpdate	VD-CMA update that is a linear time/space variant of CMA-ES This is an implementation that closely follows: Y. Akimoto, A. Auger and N. Hansen: Comparison-Based Natural Gradient Optimization in High Dimension. In Proceedings of Genetic and Evolutionary Computation Conference (2014)
CA
CA< int, U >
CB
CCMAAXLen
CCMAFit
CCMAStdDev
CCMAXMean
CCMAXRecentBest
CcustomCMAStrategy
CDC2DSurvey
CHeader
ClastEvalStruct
CLegacyCMAOutput
CparamStruct
CRelBreitWigner
CRSVMSimpleSurrogateStrategy
CSqrtEigenVals
CStaticDescriptorInitializer_out_2eproto
CStaticDescriptorInitializer_out_5fext_2eproto
CStds
CtwoDoubles
CUniqueCMAOutput
CXMean