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Genetic Algorithm for Database Indexing

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

In this paper we propose a new method for improving efficiency of database systems, using genetic algorithms. We show how table indexes can be coded in chromosomes, and a way to automatically find the best set of indexes for databases.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Częstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Marcin Korytkowski, Marcin Gabryel, Robert Nowicki & Rafał Scherer

  2. Department of Artificial Intelligence, WSHE University in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Robert Nowicki & Rafał Scherer

Authors
  1. Marcin Korytkowski
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  2. Marcin Gabryel
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  3. Robert Nowicki
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  4. Rafał Scherer
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Korytkowski, M., Gabryel, M., Nowicki, R., Scherer, R. (2004). Genetic Algorithm for Database Indexing. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_179

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_179

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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