SCIENCE@home

LLAMA: Leveraging Learning to Automatically Manage Algorithms

Lars Kotthoff

Published 2013 in arXiv.org

ABSTRACT

Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    arXiv.org

  • Publication date

    2013-06-05

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1306.1031
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-27 of 27 references · Page 1 of 1

CITED BY

Showing 1-43 of 43 citing papers · Page 1 of 1