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Optimising Graph Partitions Using Parallel Evolution

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Artificial Evolution (EA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2936))

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Abstract

The graph partitioning problem consists of dividing the vertices of a graph into a set of balanced parts, such that the number of edges connecting vertices in different parts is minimised. Although different algorithms to solve this problem have been proposed in complex graphs, it is unknown how good the partitions are since the problem is, in general, NP-complete. In this paper we present a new parallel evolutionary algorithm for graph partitioning where different heuristics, such Simulated Annealing, Tabu Search, and some Selection Mechanisms are mixed. The efficiency of the new algorithm is compared with other previously proposed algorithms with promising results.

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References

  1. Alpert, C.J., Kahng, A.: Recent Developments in Netlist Partitioning: A Survey. Integration: the VLSI Journal 19(1-2), 1–81 (1995)

    Article  MATH  Google Scholar 

  2. Banerjee, P.: Parallel Algorithms for VLSI Computer Aided Design. Prentice Hall, Englewoods Cliffs (1994)

    Google Scholar 

  3. Gil, C., Ortega, J., Montoya, M.G.: Parallel VLSI Test in a Shared Memory Multiprocessors. Concurrency: Practice and Experience 12(5), 311–326 (2000)

    Article  MATH  Google Scholar 

  4. Mobasher, B., Jain, N., Han, E.H., Srivastava, J.: Web mining: Pattern discovery from world wide web transactions. Technical Report TR-96-050, Department of computer science, University of Minnesota, Minneapolis (1996)

    Google Scholar 

  5. Shekhar, S., DLiu, D.R.: Partitioning similarity graphs: A framework for declustering problems. Information Systems Journal 21(6), 475–496 (1996)

    Article  Google Scholar 

  6. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Company, San Francisco (1979)

    MATH  Google Scholar 

  7. Kernighan, B.W., Lin, S.: An Efficient Heuristic Procedure for Partitioning Graphics, The Bell Sys. Tech. Journal, 291–307 (1970)

    Google Scholar 

  8. Fiduccia, C., Mattheyses, R.: A Linear Time Heuristic for Improving Network Partitions. In: Proc. 19th IEEE Design Automation Conference, pp. 175–181 (1982)

    Google Scholar 

  9. Simon, H.D., Teng, S.: How Good is Recursive Bisection? SIAM J. Scientific Computing 18(5), 1436–1445 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  10. Gilbert, J., Miller, G., Teng, S.: Geometric Mesh Partitioning: Implementation and Experiments. In: Proc. 9th Int. Parallel Processing Symposium, pp. 418–427 (1995)

    Google Scholar 

  11. Karypis, G., Kumar, V.: Multilevel K-way Partitioning Scheme for Irregular Graphs. Journal of Parallel and Distributed Computing 48(1), 96–129 (1998)

    Article  MathSciNet  Google Scholar 

  12. Cong, J., Smith, M.: A Parallel Bottom-up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design. In: Proc. ACM/IEEE Design Automation Conference, pp. 755–760 (1993)

    Google Scholar 

  13. Bui, T.N., Moon, B.: Genetic Algorithm and Graph Partitioning. IEEE Transactions on Computers 45(7), 841–855 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  14. Soper, A.J., Walshaw, C., Cross, M.: A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph Partitioning, Mathematics Research Report 00/IM/58, University of Greenwich (2000)

    Google Scholar 

  15. Gil, C., Ortega, J., Montoya, M.G., Baños, R.: A Mixed Heuristic for Circuit Partitioning. Computational Optimization and Applications Journal 23(3), 321–340 (2002)

    Article  MATH  Google Scholar 

  16. Baños, R., Gil, C., Ortega, J., Montoya, F.G.: Multilevel Heuristic Algorithm for Graph Partitioning. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 143–153. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  17. Karypis, G., Kumar, V.: A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM Journal on Scientific Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  Google Scholar 

  18. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1993)

    Article  MathSciNet  Google Scholar 

  19. Glover, F., Laguna, M.: Tabu Search. In: Reeves, C.R. (ed.) Modern Heuristic Techniques for Combinatorial Problems, pp. 70–150. Blackwell, London (1993)

    Google Scholar 

  20. Cantu-Paz, E.: A Survey of Parallel Genetic Algorithms. Technical Report IlliGAL 97003, University of Illinois at Urbana-Champaign (1997)

    Google Scholar 

  21. Graph Partitioning Archive, http://www.gre.ac.uk/c.walshaw/partition/ URL time: April 25 (2003), 2045

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Author information

Authors and Affiliations

  1. Dept. Arquitectura de Computadores y Electronica, Universidad de Almeria, La Cañada de San Urbano s/n, 04120, Almeria, Spain

    R. Baños & C. Gil

  2. Dept. Arquitectura y Tecnologia de Computadores, Universidad de Granada, Campus de Fuentenueva, Granada, Spain

    J. Ortega

  3. Dept. Ingenieria Civil, Universidad de Granada, Campus de Fuentenueva, Granada, Spain

    F. G. Montoya

Authors
  1. R. Baños
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  2. C. Gil
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  3. J. Ortega
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  4. F. G. Montoya
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Editor information

Editors and Affiliations

  1. UMR-CNRS 6632, Université de Provence, 39 rue F. Joliot-Curie, 13453, Marseille cedex 13, France

    Pierre Liardet

  2. Université Louis Pasteur, LSIIT, FDBT, Pôle API, F-67400, Illkirch, France

    Pierre Collet

  3. Laboratoire d’Informatique du Littoral, Maison de la Recherche Blaise Pascal, 50 rue Ferdinand Buisson, BP 719, 62228, Calais Cedex, France

    Cyril Fonlupt

  4. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, Orsay Cedex, France

    Evelyne Lutton

  5. TAO, INRIA Saclay & LRI (UMR CNRS 8623), Bât 490, Université Paris-Sud, 91405, Orsay Cedex, France

    Marc Schoenauer

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© 2004 Springer-Verlag Berlin Heidelberg

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Baños, R., Gil, C., Ortega, J., Montoya, F.G. (2004). Optimising Graph Partitions Using Parallel Evolution. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2003. Lecture Notes in Computer Science, vol 2936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24621-3_8

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  • DOI: https://doi.org/10.1007/978-3-540-24621-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21523-3

  • Online ISBN: 978-3-540-24621-3

  • eBook Packages: Springer Book Archive

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