A Reinforcement Learning Inspired Latent Yield Based Adaptive Algorithm Switching Mechanism
Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where problem characteristics evolve over time. Relying solely on instantaneous performance metrics can result in a reactive and unstable behaviour, often leading to suboptimal algorithm switching. This paper introduces a computationally efficient approach for aggregating an algorithm's performance across multiple problem instances that is fairly immune to erratic variations in instance features. Inspired by features inherent to Reinforcement Learning (RL), this technique encapsulates rewards and penalties into a latent yield that, in turn, triggers...
| Authors | Jayprakash S. Nair, Jimson Mathew, Shivashankar B. Nair |
|---|---|
| Primary category | cs.MA |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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