Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-level Framework

  • Cédric Chevalier
  • François Pellegrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4128)


Parallel graph partitioning is a difficult issue, because the best sequential graph partitioning methods known to date are based on iterative local optimization algorithms that do not parallelize nor scale well. On the other hand, evolutionary algorithms are highly parallel and scalable, but converge very slowly as problem size increases. This paper presents methods that can be used to reduce problem space in a dramatic way when using graph partitioning techniques in a multi-level framework, thus enabling the use of evolutionary algorithms as possible candidates, among others, for the realization of efficient scalable parallel graph partitioning tools. Results obtained on the recursive bipartitioning problem with a multi-threaded genetic algorithm are presented, which show that this approach outperforms existing state-of-the-art parallel partitioners.


Genetic Algorithm Memetic Algorithm Large Graph Graph Partitioning Nest Dissection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cédric Chevalier
    • 1
  • François Pellegrini
    • 1
  1. 1.LaBRI and INRIA FutursUniversité Bordeaux ITALENCEFrance

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