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OPTION: OPTImization Algorithm Benchmarking ONtology
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AI
DOI：10.1109/TEVC.2022.3232844.png)
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Abstract
En 中文
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this article, we report on the development of such an ontology, which we call 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 automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and yet another black-box optimization benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base (KB) using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION KB into the IOHprofiler environment and provide users with the ability to perform a meta-analysis of performance data.
Keywords:
Ontologies
Benchmark testing
Optimization
Resource description framework
Semantics
Annotations
Semantic Web
Benchmarking
ontology
optimization
semantic annotation
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W
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