about.md
In IOHanalyzer: Data Analysis Part of 'IOHprofiler'

Iterative Optimization Heuristics Profiler

The performance analyzer for Iterative Optimization Heuristics (IOHs). It provides:

a web-based interface to analyze and visualize the empirical performance of IOHs
interactive plot
statistical evaluation
report generation
R programming interfaces in the backend

Hao Wang, Leiden Institute of Advanced Computer Science.
Diederick Vermetten, Leiden Institute of Advanced Computer Science.
Furong Ye, Leiden Institute of Advanced Computer Science.
Carola Doerr, CNRS and Sorbonne University.
Ofer Shir Migal - The Galilee Research Institute, Tel-Hai College.
Thomas Bäck, Leiden Institute of Advanced Computer Science.

[mandatory] R As IOHanalyzer is written as a R package, the R environment has to be installed first. The binary file and installation manual for R can be found here https://cran.r-project.org/.
[optional] orca required to download plotly figures. Please see https://github.com/plotly/orca for the installation instruction.
[optional] inkscape required to support pdf and eps figure format in downloading. Please visit the Inskscape Wiki page http://wiki.inkscape.org/wiki/index.php/Installing_Inkscape for the detail.

We provide docker file for deploying IOHanalyzer on the server. Please see https://github.com/IOHprofiler/IOHanalyzer-docker for details.

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{IOHprofiler,
  author = {Carola Doerr and Hao Wang and Furong Ye and Sander van Rijn and Thomas B{\"a}ck},
  title = {{IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics}},
  journal = {arXiv e-prints:1810.05281},
  archivePrefix = "arXiv",
  eprint = {1810.05281},
  year = 2018,
  month = oct,
  keywords = {Computer Science - Neural and Evolutionary Computing},
  url = {https://arxiv.org/abs/1810.05281}
}

This work was supported by the Chinese scholarship council (CSC No. 201706310143), a public grant as part of the Investissement d’avenir project, reference ANR-11-LABX-0056-LMH, LabEx LMH, in a joint call with the Gaspard Monge Program for optimization, operations research, and their interactions with data sciences, by Paris Ile-de-France Region, and by COST Action CA15140 “Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)”.

This application is governed by the BSD 3-Clause license.

BSD 3-Clause License

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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