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Hybrid Genetic Algorithm for Solving the p-Median Problem

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Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

The p-median problem is an NP-complete combinatorial optimisation problem well investigated in the fields of facility location and more recently, clustering and knowledge discovery. We show that hybrid optimisation algorithms provide reasonable speed and high quality of solutions, allowing effective trade-of of quality of the solution with computational effort. Our approach to hybridisation is a tightly coupled approach rather than a serialisation of hill-climbers with genetic algorithms. Our hybrid algorithms use genetic operators that have some memory about how they operated in their last invocation.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Software Engineering, The University of Newcastle, Callaghan, 2308, NSW, Australia

    Vladimir Estivill-Castro

  2. Institut d’Investigacifó en Intelligencia Artificial (IIIA), Spanish Scientific Research Council (CSIC), Campus Universitat Autónoma de Barcelona, 08193, Bellaterra, Barcelona, Spain

    Rodolfo Torres-Velázquez

Authors
  1. Vladimir Estivill-Castro
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  2. Rodolfo Torres-Velázquez
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Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

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© 1999 Springer-Verlag Berlin Heidelberg

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Estivill-Castro, V., Torres-Velázquez, R. (1999). Hybrid Genetic Algorithm for Solving the p-Median Problem. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_4

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  • DOI: https://doi.org/10.1007/3-540-48873-1_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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