BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization

[2608.15073] BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computational Engineering, Finance, and Science arXiv:2608.15073 (cs) [Submitted on 15 Aug 2026] Title: BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization Authors: Rosen Ting-Ying Yu , Christophe Hatterer , Advaith Narayanan , Cyril Picard , Faez Ahmed View a PDF of the paper titled BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization, by Rosen Ting-Ying Yu and 4 other authors View PDF HTML (experimental) Abstract: Bayesian optimization (BO) is a sample-efficient, surrogate-based approach to black-box optimization (BBO), but its evaluation remains dominated by synthetic functions and hyperparameter optimization (HPO) tasks that are typically low-dimensional and single-objective. Engineering design poses a substantially different regime: problems are physics-based, often high-dimensional, constrained by requirements such as cost and manufacturability, and may involve multiple objectives or mixed variables. To close this benchmarking gap, we introduce BOCoDe, an open-source, PyTorch-native benchmark comprising 307 BBO problems, including 159 engineering design tasks and widely used synthetic and HPO benchmarks. Each problem includes cited provenance and machine-readable metadata that supports programmatic discovery, including by LLM-based agents, and all tasks are exposed through a unified API compatible with open-source BO libraries. We evaluate 31 BO and evolutionary algorithms across five problem classes spanning single- and multi-objective optimization, constrained and unconstrained settings, and mixed-variable search spaces. Analyses of problem structure show that engineering tasks uniquely span constrained and multi-objective settings that synthetic and HPO suites rarely cover, while embeddings from a tabular foundation model separate them most clearly from HPO tasks. Algorithm rankings also vary substantially across domains; in several problem classes, rankings obtained on standard benchmarks do not transfer to engineering tasks. BOCoDe establishes a reproducible and extensible foundation for developing and evaluating BO methods that better reflect the demands of engineering design. Code & data can be found at this https URL Subjects: Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2608.15073 [cs.CE] (or arXiv:2608.15073v1 [cs.CE] for this version) https://doi.org/10.48550/arXiv.2608.15073 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rosen Ting-Ying Yu [ view email ] [v1] Sat, 15 Aug 2026 06:47:28 UTC (6,753 KB) Full-text links: Access Paper: View a PDF of the paper titled BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization, by Rosen Ting-Ying Yu and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CE < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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