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Annual Conference on Artificial Intelligence

KI 2009: KI 2009: Advances in Artificial Intelligence pp 25–32Cite as

A Memory-Efficient Search Strategy for Multiobjective Shortest Path Problems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

Abstract

The paper develops vector frontier search, a new multiobjective search strategy that achieves an important reduction in space requirements over previous proposals. The complexity of a resulting multiobjective frontier search algorithm is analyzed and its performance is evaluated over a set of random problems.

This work is partially funded by/Este trabajo está parcialmente financiado por: Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía (España), P07-TIC-03018.

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References

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Author information

Authors and Affiliations

  1. Dpto. Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071, Málaga, Spain

    L. Mandow & J. L. Pérez de la Cruz

Authors
  1. L. Mandow
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  2. J. L. Pérez de la Cruz
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Editor information

Editors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Bärbel Mertsching , Marcus Hund  & Zaheer Aziz ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Mandow, L., de la Cruz, J.L.P. (2009). A Memory-Efficient Search Strategy for Multiobjective Shortest Path Problems. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_4

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  • DOI: https://doi.org/10.1007/978-3-642-04617-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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