Track seeding strongly affects both the quality and computational cost of charged-particle reconstruction, yet its many configuration parameters are commonly tuned through expert intuition and repeated trial and error. ACTS reduces this burden with an Optuna Tree-structured Parzen Estimator auto-tuner, but expensive evaluations, a restricted search space, and a scalarized objective can limit evaluation efficiency, exclude promising configurations, and obscure performance trade-offs. We investigate whether Bayesian optimization can address these limitations using ACTS with the Open Data Detector (ODD). Under identical search ranges and a common 100-trial budget, we compare Expected Improvement and Upper Confidence Bound with TPE and random search on the existing eight-parameter problem, extend the best-performing Bayesian method to fifteen parameters, and apply Expected Hypervolume Improvement to optimize efficiency, fake rate, duplicate rate, and runtime without fixed scalar weights. Candidate configurations are evaluated through the full ACTS reconstruction chain and validated on disjoint held-out events. The Bayesian acquisition methods identify strong configurations earlier than TPE, and their advantage persists in held-out validation. Expanding the search further improves performance, while multi-objective optimization reveals competitive non-dominated solutions spanning distinct trade-offs. These results indicate that Bayesian optimization can strengthen ACTS auto-tuning through efficient evaluation, broader parameter searches, and post-hoc expert selection among non-dominated alternatives.
JANUS is a plug-and-play infill module that extracts a local Jacobian from the recent evaluation trace, and gives the best mean cost on 1135-dimensional UAV path planning and improves SMS-EMOA/AGE-MOEA2 hosts on 12/38 multi-objective tasks with zero significant regressions.
Hongyuan Yu, Pufan Xu, Jiaojiao Yi et al.· 0 citations
Optizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian optimization, improves both simple and sophisticated samplers such as Euler and DPM-Solver++.
Travis Zhang, Christian K. Belardi, Justin Lovelace et al.· 0 citations
Reliable global optimization of atomic clusters is limited not only by the ruggedness of the potential energy surface but also by the quality of the starting population supplied to the search algorithm. We examine this issue for the Bonobo Optimizer (BO) using a workflow that combines external PyAR-style population initialization, local Lennard-Jones relaxation, geometry repair, and structural similarity filtering. The initializer generates physically reasonable and structurally diverse trial clusters before the BO loop begins, while the relaxation and filtering steps map candidates to local minima and reduce redundant population updates. On Lennard-Jones clusters, the unmodified BO framework becomes unreliable beyond LJ13, and local relaxation with similarity filtering alone remains insufficient for the double-funnel LJ38 benchmark, reaching the global minimum only six times in 25 runs. Adding the Tabu-style initializer changes this outcome: LJ38 is solved in all 20 independent runs, and the full LJ2-LJ100 benchmark reaches 943 successes in 1030 runs, corresponding to an overall success rate of 91.5%. The main remaining failures occur for LJ75-LJ77, consistent with the known difficulty of the Marks-decahedral exceptions. These results identify physically informed population construction as a practical route for improving BO-based cluster global optimization.
Bhrigu Chakraborty, A. Anoop· Journal of Computational Che...· 0 citations
BOCoDe is introduced, an open-source, PyTorch-native benchmark comprising 307 BBO problems, including 159 engineering design tasks and widely used synthetic and HPO benchmarks that establish a reproducible and extensible foundation for developing and evaluating BO methods that better reflect the demands of engineering design.
Rosen Yu, Christophe Hatterer, A. Narayanan et al.· 0 citations
This work addresses the challenging problem of multiobjective optimization via stochastic simulation over a discrete design space via stochastic simulation over a discrete design space by integrating a discrete Genetic Algorithm with specialized local and jump mutation operators as the inner optimizer.
Sebastián Rojas Gónzalez, I. Couckuyt, Joshua Knowles· Annual Conference on Genetic...· 0 citations
A unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), is proposed that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies and extends the approach to multi-objective optimization via random scalarization that preserves the single-optimizer conditioning structure.
Heng Zhang, Haotian Xiang, Qin Lu et al.· 0 citations
