Learning dynamic algorithm portfolios

  title={Learning dynamic algorithm portfolios},
  author={Matteo Gagliolo and J{\"u}rgen Schmidhuber},
  journal={Annals of Mathematics and Artificial Intelligence},
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… CONTINUE READING
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