{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iohprofiler-a-benchmarking-and-profiling-tool","title":"IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics","arxiv_id":"1810.05281","date":"2018-10-11","proceeding":null,"authors":["Carola Doerr","Hao Wang","Furong Ye","Sander van Rijn","Thomas Bäck"],"abstract":"IOHprofiler is a new tool for analyzing and comparing iterative optimization\nheuristics. Given as input algorithms and problems written in C or Python, it\nprovides as output a statistical evaluation of the algorithms' performance by\nmeans of the distribution on the fixed-target running time and the fixed-budget\nfunction values. In addition, IOHprofiler also allows to track the evolution of\nalgorithm parameters, making our tool particularly useful for the analysis,\ncomparison, and design of (self-)adaptive algorithms.\n  IOHprofiler is a ready-to-use software. It consists of two parts: an\nexperimental part, which generates the running time data, and a post-processing\npart, which produces the summarizing comparisons and statistical evaluations.\nThe experimental part is build on the COCO software, which has been adjusted to\ncope with optimization problems that are formulated as functions\n$f:\\mathcal{S}^n \\to \\R$ with $\\mathcal{S}$ being a discrete alphabet of\nintegers. The post-processing part is our own work. It can be used as a\nstand-alone tool for the evaluation of running time data of arbitrary benchmark\nproblems. It accepts as input files not only the output files of IOHprofiler,\nbut also original COCO data files. The post-processing tool is designed for an\ninteractive evaluation, allowing the user to chose the ranges and the precision\nof the displayed data according to his/her needs.\n  IOHprofiler is available on GitHub at \\url{https://github.com/IOHprofiler}.","url_abs":"http://arxiv.org/abs/1810.05281v1","url_pdf":"http://arxiv.org/pdf/1810.05281v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iohprofiler-a-benchmarking-and-profiling-tool","repo_url":"https://github.com/IOHprofiler/Analyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"iohprofiler-a-benchmarking-and-profiling-tool","repo_url":"https://github.com/IOHprofiler/IOHAnalyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"iohprofiler-a-benchmarking-and-profiling-tool","repo_url":"https://github.com/IOHprofiler/IOHexperimenter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"iohprofiler-a-benchmarking-and-profiling-tool","repo_url":"https://github.com/IOHprofiler/Post-Processing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"iohprofiler-a-benchmarking-and-profiling-tool","repo_url":"https://github.com/nojhan/IOHexperimenter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.05281","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}