Learning dynamic algorithm portfolios |
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Authors: | Matteo Gagliolo Jürgen Schmidhuber |
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Affiliation: | (1) IDSIA, Galleria 2, 6928 Manno (Lugano), Switzerland;(2) Faculty of Informatics, University of Lugano, Via Buffi 13, 6904 Lugano, Switzerland;(3) TU Munich, Boltzmannstr. 3, 85748, Garching, München, Germany |
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Abstract: | Algorithm selection can be performed using a model of runtime distribution, learned during a preliminary training phase. There
is a trade-off between the performance of model-based algorithm selection, and the cost of learning the model. In this paper,
we treat this trade-off in the context of bandit problems. We propose a fully dynamic and online algorithm selection technique,
with no separate training phase: all candidate algorithms are run in parallel, while a model incrementally learns their runtime
distributions. A redundant set of time allocators uses the partially trained model to propose machine time shares for the algorithms. A bandit problem solver mixes the model-based
shares with a uniform share, gradually increasing the impact of the best time allocators as the model improves. We present
experiments with a set of SAT solvers on a mixed SAT-UNSAT benchmark; and with a set of solvers for the Auction Winner Determination
problem.
This work was supported by SNF grant 200020-107590/1. |
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Keywords: | algorithm selection algorithm portfolios online learning life-long learning bandit problem expert advice survival analysis satisfiability constraint programming |
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