Pareto-Optimal Anytime Algorithms via Bayesian Racing
Selecting an optimization algorithm requires comparing candidates across problem instances, but the computational budget for deployment is often unknown at benchmarking time. Current methods either collapse anytime performance into a scalar, require manual interpretation of plots, or produce conclusions that change when algorithms are added or removed. Moreover, methods based on raw objective values require normalization, which needs bounds or optima that are often unavailable and breaks coherent aggregation across instances. We propose a framework that formulates anytime algorithm comparison as Pareto optimization over time: an algorithm is non-dominated if no competitor beats it at ever...
| Authors | Jonathan Wurth, Helena Stegherr, Neele Kemper et al. |
|---|---|
| Primary category | cs.NE |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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