Dynamic algorithm selection using reinforcement learning
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]
|Collections||ANU Research Publications|
|Source:||International Workshop on Integrating AI and Data Mining (AIDM 2006) Proceedings|
|01_Armstrong_Dynamic_algorithm_selection_2006.pdf||359.88 kB||Adobe PDF||Request a copy|
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