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Dynamic algorithm selection using reinforcement learning

Armstrong, Warren; Christen, Peter; McCreath, Eric; Rendell, Alistair


It is often the case that many algorithms exist to solve a single problem, each possessing different performance characteristics. The usual approach in this situation is to manually select the algorithm which has the best average performance. However, this strategy has drawbacks in cases where the optimal algorithm changes during an invocation of the program, in response to changes in the program's state and the computational environment. This paper presents a prototype tool that uses...[Show more]

CollectionsANU Research Publications
Date published: 2006
Type: Conference paper
Source: International Workshop on Integrating AI and Data Mining (AIDM 2006) Proceedings
DOI: 10.1109/AIDM.2006.4


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