%global _empty_manifest_terminate_build 0 Name: python-ioh Version: 0.3.9 Release: 1 Summary: The experimenter for Iterative Optimization Heuristics License: BSD URL: https://iohprofiler.github.io/IOHexperimenter Source0: https://mirrors.nju.edu.cn/pypi/web/packages/80/d9/31320b3dcc2984a17b28202682a03a2ba5e780e93fdb0c247dc260b5d035/ioh-0.3.9.tar.gz Requires: python3-numpy %description # IOHexperimenter ![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) ![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) ![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) **Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. * **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). * **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). * **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). **IOHexperimenter** *provides*: * A framework to ease the benchmarking of any iterative optimization heuristic. * [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). * Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). * [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. * Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. * Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). * Flexible interface for adding new suites and problems. * Advanced logging module that takes care of registering the data in a seamless manner. * Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). **Available Problem Suites:** * BBOB (Single Objective Noiseless) (COCO) * SBOX-COST (COCO) * StarDiscrepancy * PBO * Submodular Graph Problems ## C++ The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. ## Python The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). ## Contact If you have any questions, comments or suggestions, please don't hesitate contacting us . ### Our team * [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, * [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, * [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, * [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, * [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, * [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, When using IOHprofiler and parts thereof, please kindly cite this work as Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, *IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. ```bibtex @ARTICLE{IOHexperimenter, author = {Jacob de Nobel and Furong Ye and Diederick Vermetten and Hao Wang and Carola Doerr and Thomas B{\"{a}}ck}, title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, journal = {arXiv e-prints:2111.04077}, archivePrefix = "arXiv", eprint = {2111.04077}, year = 2021, month = Nov, keywords = {Computer Science - Neural and Evolutionary Computing}, url = {https://arxiv.org/abs/2111.04077} } ``` %package -n python3-ioh Summary: The experimenter for Iterative Optimization Heuristics Provides: python-ioh BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-ioh # IOHexperimenter ![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) ![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) ![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) **Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. * **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). * **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). * **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). **IOHexperimenter** *provides*: * A framework to ease the benchmarking of any iterative optimization heuristic. * [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). * Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). * [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. * Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. * Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). * Flexible interface for adding new suites and problems. * Advanced logging module that takes care of registering the data in a seamless manner. * Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). **Available Problem Suites:** * BBOB (Single Objective Noiseless) (COCO) * SBOX-COST (COCO) * StarDiscrepancy * PBO * Submodular Graph Problems ## C++ The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. ## Python The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). ## Contact If you have any questions, comments or suggestions, please don't hesitate contacting us . ### Our team * [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, * [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, * [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, * [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, * [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, * [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, When using IOHprofiler and parts thereof, please kindly cite this work as Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, *IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. ```bibtex @ARTICLE{IOHexperimenter, author = {Jacob de Nobel and Furong Ye and Diederick Vermetten and Hao Wang and Carola Doerr and Thomas B{\"{a}}ck}, title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, journal = {arXiv e-prints:2111.04077}, archivePrefix = "arXiv", eprint = {2111.04077}, year = 2021, month = Nov, keywords = {Computer Science - Neural and Evolutionary Computing}, url = {https://arxiv.org/abs/2111.04077} } ``` %package help Summary: Development documents and examples for ioh Provides: python3-ioh-doc %description help # IOHexperimenter ![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) ![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) ![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) **Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. * **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). * **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). * **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). **IOHexperimenter** *provides*: * A framework to ease the benchmarking of any iterative optimization heuristic. * [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). * Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). * [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. * Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. * Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). * Flexible interface for adding new suites and problems. * Advanced logging module that takes care of registering the data in a seamless manner. * Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). **Available Problem Suites:** * BBOB (Single Objective Noiseless) (COCO) * SBOX-COST (COCO) * StarDiscrepancy * PBO * Submodular Graph Problems ## C++ The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. ## Python The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). ## Contact If you have any questions, comments or suggestions, please don't hesitate contacting us . ### Our team * [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, * [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, * [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, * [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, * [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, * [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, When using IOHprofiler and parts thereof, please kindly cite this work as Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, *IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. ```bibtex @ARTICLE{IOHexperimenter, author = {Jacob de Nobel and Furong Ye and Diederick Vermetten and Hao Wang and Carola Doerr and Thomas B{\"{a}}ck}, title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, journal = {arXiv e-prints:2111.04077}, archivePrefix = "arXiv", eprint = {2111.04077}, year = 2021, month = Nov, keywords = {Computer Science - Neural and Evolutionary Computing}, url = {https://arxiv.org/abs/2111.04077} } ``` %prep %autosetup -n ioh-0.3.9 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-ioh -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.3.9-1 - Package Spec generated