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libcmaes
A C++11 library for stochastic optimization with CMA-ES
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libcmaes
A C++11 library for stochastic optimization with CMA-ES
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| CA< T, U > | |
| CA< int, U > | |
| Clibcmaes::ACovarianceUpdate | 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 |
| CB< V > | |
| ▼Clibcmaes::Candidate | Candidate solution point, in function parameter space |
| Clibcmaes::RankedCandidate | |
| Clibcmaes::CMASolutions | Holder of the set of evolving solutions from running an instance of CMA-ES |
| Clibcmaes::CMAStopCriteria< TGenoPheno > | CMA-ES termination criteria, see reference paper in cmastrategy.h |
| Clibcmaes::contour | Function contour as a set of points and values |
| Clibcmaes::CovarianceUpdate | 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 |
| CDC2DSurvey | |
| CEigen::EigenMultivariateNormal< Scalar > | |
| CEigen::EigenMultivariateNormal< double > | |
| Clibcmaes::errstats< TGenoPheno > | |
| Clibcmaes::ESOStrategy< TParameters, TSolutions, TStopCriteria > | Main class describing an evolutionary optimization strategy. Every algorithm in libcmaes descends from this class, and bring its functionalities to an ESOptimizer object |
| ▼Clibcmaes::ESOStrategy< CMAParameters< TGenoPheno >, CMASolutions, CMAStopCriteria< TGenoPheno > > | |
| ▼Clibcmaes::CMAStrategy< CovarianceUpdate > | |
| CcustomCMAStrategy | |
| ▼Clibcmaes::CMAStrategy< TCovarianceUpdate, TGenoPheno > | 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 |
| ▼Clibcmaes::IPOPCMAStrategy< TCovarianceUpdate, TGenoPheno > | 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 |
| Clibcmaes::BIPOPCMAStrategy< TCovarianceUpdate, TGenoPheno > | 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 |
| Clibcmaes::OptHopStrategy< TCovarianceUpdate, TGenoPheno > | |
| ▼Clibcmaes::SurrogateStrategy< CMAStrategy, TCovarianceUpdate, TGenoPheno > | |
| ▼Clibcmaes::SimpleSurrogateStrategy< CMAStrategy, TCovarianceUpdate, TGenoPheno > | |
| CRSVMSimpleSurrogateStrategy< TCovarianceUpdate, TGenoPheno > | |
| Clibcmaes::fcross | Function crossing as point |
| CEigen::internal::functor_traits< scalar_normal_dist_op< Scalar > > | |
| Clibcmaes::GenoPheno< TBoundStrategy, TScalingStrategy > | |
| ClastEvalStruct | |
| Clibcmaes::linScalingStrategy | |
| ▼CMessage | |
| CCMAAXLen | |
| CCMAFit | |
| CCMAStdDev | |
| CCMAXMean | |
| CCMAXRecentBest | |
| CHeader | |
| CLegacyCMAOutput | |
| CSqrtEigenVals | |
| CStds | |
| CUniqueCMAOutput | |
| CXMean | |
| Clibcmaes::NoBoundStrategy | |
| Clibcmaes::NoScalingStrategy | |
| ▼Clibcmaes::Parameters< TGenoPheno > | Generic class for Evolution Strategy parameters |
| Clibcmaes::CMAParameters< TGenoPheno > | Parameters for various flavors of the CMA-ES algorithm |
| CparamStruct | |
| Clibcmaes::pli | Profile likelihood object holder as a set of points and values |
| Clibcmaes::pwqBoundStrategy | |
| CRelBreitWigner | |
| CEigen::internal::scalar_normal_dist_op< Scalar > | |
| CEigen::internal::scalar_normal_dist_op< double > | |
| CStaticDescriptorInitializer_out_2eproto | |
| CStaticDescriptorInitializer_out_5fext_2eproto | |
| Clibcmaes::StopCriteria< TGenoPheno > | |
| ▼CTESOStrategy | |
| Clibcmaes::ESOptimizer< TESOStrategy, TParameters, TSolutions > | Optimizer main class |
| ▼CTStrategy | |
| ▼Clibcmaes::SurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | Surrogate base class, to be derived in order to create strategy to be used along with CMA-ES |
| Clibcmaes::ACMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | 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 |
| Clibcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | 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 |
| CtwoDoubles | |
| Clibcmaes::VDCMAUpdate | 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) |
1.8.8
