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Experiences with fine‐grainedparallel genetic algorithms

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  • U. Kohlmorgen
  • H. Schmeck
  • K. Haase

Abstract

In this paper, we present some results of our systematic studies of fine‐grained parallelversions of the island model of genetic algorithms and of variants of the neighborhood model(also called diffusion model) on the massively parallel computer MasPar MP1 with 16kprocessing elements. These parallel genetic algorithms have been applied to a range ofdifferent problems (e.g. traveling salesman, capacitated lot sizing, resource‐constrainedproject scheduling, flow shop, and warehouse location problems) in order to obtain anempirical basis for statements on their optimization quality. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • U. Kohlmorgen & H. Schmeck & K. Haase, 1999. "Experiences with fine‐grainedparallel genetic algorithms," Annals of Operations Research, Springer, vol. 90(0), pages 203-219, January.
    • Handle: RePEc:spr:annopr:v:90:y:1999:i:0:p:203-219:10.1023/a:1018912715283
    DOI: 10.1023/A:1018912715283
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    Cited by:

    1. Kolisch, R. & Padman, R., 2001. "An integrated survey of deterministic project scheduling," Omega, Elsevier, vol. 29(3), pages 249-272, June.
    2. Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre (Ed.), 2000. "Jahresbericht 1999," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 522, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    3. Moumene, Khaled & Ferland, Jacques A., 2009. "Activity list representation for a generalization of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 199(1), pages 46-54, November.
    4. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.

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