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International Conference on Parallel Problem Solving from Nature

PPSN 1998: Parallel Problem Solving from Nature — PPSN V pp 907–916Cite as

A permutation based Genetic Algorithm for minimum span frequency assignment

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

Abstract

We describe a Genetic Algorithm (GA) for solving the minimum span frequency assignment problem (MSFAP).The MSFAP involves assigning frequencies to each transmitter in a region, subject to a number of constraints being satisfied, such that the span, i.e. the range of frequencies used, is minimized. The technique involves finding an ordering of the transmitters for use in a sequential (greedy) assignment process. Results are given which show that our GA produces optimal solutions to several practical problem instances, and compares favourably to simulated annealing and tabu search algorithms.

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Author information

Author notes

  1. Christine Valenzuela

    Present address: Department of Computer Science, George Mason University, Virginia, USA

Authors and Affiliations

  1. School of Computing and Mathematics, University of Teesside, TS1 3BA, UK

    Christine Valenzuela

  2. Department of Computer Science, Cardiff University, CF2 3XF, UK

    Steve Hurley

  3. Division of Mathematics and Computing, University of Glamorgan, CF37 1DL, UK

    Derek Smith

Authors
  1. Christine Valenzuela
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  2. Steve Hurley
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  3. Derek Smith
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Valenzuela, C., Hurley, S., Smith, D. (1998). A permutation based Genetic Algorithm for minimum span frequency assignment. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056932

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  • DOI: https://doi.org/10.1007/BFb0056932

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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