When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
Online algorithm selection (OAS) aims to adapt the optimization process to changes in the fitness landscape and is expected to outperform any single algorithm from a given portfolio. Although this expectation is supported by numerous empirical studies, there are currently no theoretical results proving that OAS can yield asymptotic speedups (apart from some artificial examples for hyper-heuristics). Moreover, theory-based guidelines for when and how to switch between algorithms are largely missing. In this paper, we present the first theoretical example in which switching between two algorithms -- the $(1+λ)$ EA and the $(1+(λ,λ))$ GA -- solves the OneMax problem asymptotically faster tha...
| Authors | Denis Antipov, Carola Doerr |
|---|---|
| Primary category | cs.NE |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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