IOHexperimenter

Experimenter for Iterative Optimization Heuristics (IOHs), built natively 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)
• 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

C++

The interface for C++ interface is described in more detail in the wiki. The complete API documentation, including some usage examples, can be found here.

Python

A quickstart for the Python interface and the full API documentation can be found here. It is also described in the wiki and available via pip.

Contact

If you have any questions, comments or suggestions, please don't hesitate contacting us [email protected].

Our team

• 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}
}