1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
|
%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
**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 <IOHprofiler@liacs.leidenuniv.nl>.
### 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
**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 <IOHprofiler@liacs.leidenuniv.nl>.
### 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
**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 <IOHprofiler@liacs.leidenuniv.nl>.
### 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 <Python_Bot@openeuler.org> - 0.3.9-1
- Package Spec generated
|
