|
libcmaes
A C++11 library for stochastic optimization with CMA-ES
|
|
libcmaes
A C++11 library for stochastic optimization with CMA-ES
|
linear scaling of the parameter space to achieve similar sensitivity across all components. More...
Classes | |
| class | ACMSurrogateStrategy |
| 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. More... | |
| class | 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. More... | |
| class | BIPOPCMAStrategy |
| 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. More... | |
| class | Candidate |
| candidate solution point, in function parameter space. More... | |
| class | CMAParameters |
| Parameters for various flavors of the CMA-ES algorithm. More... | |
| class | CMASolutions |
| Holder of the set of evolving solutions from running an instance of CMA-ES. More... | |
| class | CMAStopCriteria |
| CMA-ES termination criteria, see reference paper in cmastrategy.h. More... | |
| class | CMAStrategy |
| 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. More... | |
| class | contour |
| function contour as a set of points and values. More... | |
| class | 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. More... | |
| class | errstats |
| class | ESOptimizer |
| an optimizer main class. More... | |
| class | ESOStrategy |
| Main class describing an evolutionary optimization strategy. Every algorithm in libcmaes descends from this class, and bring its functionalities to an ESOptimizer object. More... | |
| class | fcross |
| function crossing as point. More... | |
| class | GenoPheno |
| class | IPOPCMAStrategy |
| 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. More... | |
| class | linScalingStrategy |
| class | NoBoundStrategy |
| class | NoScalingStrategy |
| class | OptHopStrategy |
| class | Parameters |
| Generic class for Evolution Strategy parameters. More... | |
| class | pli |
| profile likelihood object holder as a set of points and values. More... | |
| class | pwqBoundStrategy |
| class | RankedCandidate |
| class | SimpleSurrogateStrategy |
| 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. More... | |
| class | StopCriteria |
| class | SurrogateStrategy |
| Surrogate base class, to be derived in order to create strategy to be used along with CMA-ES. More... | |
| class | 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) More... | |
Typedefs | |
| template<class TGenoPheno > | |
| using | StopCriteriaFunc = std::function< int(const CMAParameters< TGenoPheno > &cmap, const CMASolutions &cmas)> |
| template<class TGenoPheno > | |
| using | eostrat = ESOStrategy< CMAParameters< TGenoPheno >, CMASolutions, CMAStopCriteria< TGenoPheno > > |
|
typedef std::function< double(const double *, const int &n)> | FitFunc |
|
typedef std::function< dVec(const double *, const int &n)> | GradFunc |
|
typedef std::function< void(const dMat &, const dMat &)> | EvalFunc |
| typedef std::function< dMat(void)> | AskFunc |
| typedef std::function< void(void)> | TellFunc |
| template<class TParameters , class TSolutions > | |
| using | ProgressFunc = std::function< int(const TParameters &, const TSolutions &)> |
| template<class TParameters , class TSolutions > | |
| using | PlotFunc = std::function< int(const TParameters &, const TSolutions &, std::ofstream &fplotstream)> |
|
typedef std::function< void(const double *, double *, const int &)> | TransFunc |
| typedef std::function< int(const std::vector< Candidate > &, const dMat &)> | CSurrFunc |
| function to train a surrogate model More... | |
| typedef std::function< int(std::vector < Candidate > &, const dMat &)> | SurrFunc |
| function to predict from a surrogate model More... | |
Functions | |
| template<class TGenoPheno = GenoPheno<NoBoundStrategy>> | |
| CMASolutions | cmaes (FitFunc &func, CMAParameters< TGenoPheno > ¶meters, ProgressFunc< CMAParameters< TGenoPheno >, CMASolutions > &pfunc=CMAStrategy< CovarianceUpdate, TGenoPheno >::_defaultPFunc, GradFunc gfunc=nullptr, const CMASolutions &solutions=CMASolutions(), PlotFunc< CMAParameters< TGenoPheno >, CMASolutions > &pffunc=CMAStrategy< CovarianceUpdate, TGenoPheno >::_defaultFPFunc) |
| std::ostream & | operator<< (std::ostream &out, const CMASolutions &cmas) |
| double | median (std::vector< double > scores) |
| template<class TCovarianceUpdate , class TGenoPheno > | |
| int | pfuncdef_impl (const CMAParameters< TGenoPheno > &cmaparams, const CMASolutions &cmasols) |
| template<class TCovarianceUpdate , class TGenoPheno > | |
| int | fpfuncdef_full_impl (const CMAParameters< TGenoPheno > &cmaparams, const CMASolutions &cmasols, std::ofstream &fplotstream) |
| template<class TCovarianceUpdate , class TGenoPheno > | |
| int | fpfuncdef_impl (const CMAParameters< TGenoPheno > &cmaparams, const CMASolutions &cmasols, std::ofstream &fplotstream) |
| std::ostream & | operator<< (std::ostream &out, const contour &c) |
| template<typename T > | |
| int | sgn (T val) |
| std::ostream & | LOG (const std::string &severity, std::ostream &out=std::cout) |
| std::ostream & | LOG_IF (const std::string &severity, const bool &condition, std::ostream &out=std::cout) |
| template<class TGenoPheno = GenoPheno<NoBoundStrategy>> | |
| CMASolutions | surrcmaes (FitFunc &func, CMAParameters< TGenoPheno > ¶meters) |
linear scaling of the parameter space to achieve similar sensitivity across all components.
CMA-ES, Covariance Matrix Adaptation Evolution Strategy Copyright (c) 2014 Inria Author: Emmanuel Benazera emmanuel.benazera@lri.fr
This file is part of libcmaes.
libcmaes is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
libcmaes is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with libcmaes. If not, see http://www.gnu.org/licenses/.
CMA-ES, Covariance Matrix Adaptation Evolution Strategy Copyright (c) 2014 INRIA Author: Emmanuel Benazera emmanuel.benazera@lri.fr
This file is part of libcmaes.
libcmaes is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
libcmaes is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with libcmaes. If not, see http://www.gnu.org/licenses/.
CMA-ES, Covariance Matrix Adaptation Evolution Strategy Copyright (c) 2014 Inria Author: Emmanuel Benazera emmanuel.benazera@lri.fr
This file is part of libcmaes.
libcmaes is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
libcmaes is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with libcmaes. If not, see http://www.gnu.org/licenses/. This is based on Nikolaus Hansen's code from the C version of CMA-ES, see boundary_transformation.c from the package available from https://www.lri.fr/~hansen/cmaes_inmatlab.html#C
CMA-ES, Covariance Matrix Adaptation Evolution Strategy Copyright (c) 2014 Inria Author: Emmanuel Benazera emmanuel.benazera@lri.fr
This file is part of libcmaes.
libcmaes is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
libcmaes is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with libcmaes. If not, see http://www.gnu.org/licenses/. Parts of this code are rewrittent from Y. Akimoto original code for Y. Akimoto, A. Auger and N. Hansen, Comparison-Based Natural Gradient Optimization in High Dimension, GECCO-2014.
| typedef std::function<int (const std::vector<Candidate>&, const dMat&)> libcmaes::CSurrFunc |
function to train a surrogate model
| candidates | set of points along with objective function value |
| cov | a possibly empty covariance matrix in order to re-scale points before training |
| typedef std::function<int (std::vector<Candidate>&, const dMat&)> libcmaes::SurrFunc |
function to predict from a surrogate model
| candidates | set of points for which value is to be predicted |
| cov | a possibly empty covariance matrix in order to re-scale points before predicting |
1.8.8
