Experimenter for Iterative Optimization Heuristics (IOHs), built in* C++.
- Documentation: https://iohprofiler.github.io/IOHexperimenter
- Publication: https://arxiv.org/abs/1810.05281
- Wiki page: https://iohprofiler.github.io
IOHexperimenter provides:
- A framework to ease the benchmarking of any iterative optimization heuristic
- Pseudo-Boolean Optimization (PBO) problem set (25 pseudo-Boolean problems)
- Integration of the well-known Black-black Optimization Benchmarking (BBOB) problem set (24 continuous problems)
- W-model problem sets constructed on OneMax and LeadingOnes
- Integration of the Tree Decomposition (TD) Mk Landscapes problems
- Integration of the submodular optimization problems in Competition - Evolutionary Submodular Optimisation GECCO 2022
- Interface for adding new problems and suite/problem set
- Advanced logging module that takes care of registering the data in a seamless manner
- Data format is compatible with IOHanalyzer
The complete API documentation, can be found here. In addition to the documentation, some example projects can be found in the example folder of this repository.
The pip-version of IOHexperimenters python interface is available via pip. A tutorial with python in the form of a jupyter notebook can be found in the example folder of this repository, here. The full API documentation can be found here.
If you have any questions, comments or suggestions, please don't hesitate contacting us [email protected].
- Jacob de Nobel, Leiden Institute of Advanced Computer Science,
- Furong Ye, Leiden Institute of Advanced Computer Science,
- Diederick Vermetten, Leiden Institute of Advanced Computer Science,
- Hao Wang, Leiden Institute of Advanced Computer Science,
- Carola Doerr, CNRS and Sorbonne University,
- Thomas Bäck, Leiden Institute of Advanced Computer Science,
When using IOHprofiler and parts thereof, please kindly cite this work as
Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, Thomas Bäck: IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics, arXiv e-prints:1810.05281, 2018.
@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}
}