# OPTION: OPTImization Algorithm Benchmarking ONtology

arXiv: https://arxiv.org/abs/2104.11889 · HTML: https://mlsanity.com/paper/2104.11889/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2021.

## Built on by (most cited first, 9 of 9)

- [MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts](https://mlsanity.com/paper/2306.10627/) (2023)
- [The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants](https://mlsanity.com/paper/2204.07431/) (2022)
- [PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization](https://mlsanity.com/paper/2310.10685/) (2023)
- [Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms](https://mlsanity.com/paper/2301.09876/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization](https://mlsanity.com/paper/2606.26852/) (2026)
- [Explainable Model-specific Algorithm Selection for Multi-Label Classification](https://mlsanity.com/paper/2211.11227/) (2022)
- [From Performance to Understanding: A Vision for Explainable Automated Algorithm Design](https://mlsanity.com/paper/2511.16201/) (2025)
- [Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network](https://mlsanity.com/paper/2512.16491/) (2025)

## Abstract (arXiv)

Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research. However, different platforms use different data models and formats, which drastically inhibits identification of relevant data sets, their interpretation, and their interoperability. Consequently, a semantically rich, ontology-based, machine-readable data model is highly desired. We report in this paper on the development of such an ontology, which we name OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automated data integration, improved interoperability, powerful querying capabilities and reasoning, thereby enriching the value of the benchmark data. We demonstrate the utility of OPTION by annotating and querying a corpus of benchmark performance data from the BBOB workshop data - a use case which can be easily extended to cover other benchmarking data collections.