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Towards Distributed Algorithm Portfolios

  • Matteo Gagliolo
  • Jürgen Schmidhuber
Part of the Advances in Soft Computing book series (AINSC, volume 50)

Summary

In recent work we have developed an online algorithm selection technique, in which a model of algorithm performance is learned incrementally while being used. The resulting exploration-exploitation trade-off is solved as a bandit problem. The candidate solvers are run in parallel on a single machine, as an algorithm portfolio, and computation time is shared among them according to their expected performances. In this paper, we extend our technique to the more interesting and practical case of multiple CPUs.

Keywords

Problem Instance Algorithm Selection Graph Coloring Bandit Problem Optimal Share 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matteo Gagliolo
    • 1
    • 2
  • Jürgen Schmidhuber
    • 1
    • 2
    • 3
  1. 1.IDSIAManno (Lugano)Switzerland
  2. 2.Faculty of InformaticsUniversity of LuganoLuganoSwitzerland
  3. 3.TU MunichMünchenGermany

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