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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2002: Applications of Evolutionary Computing pp 61–71Cite as

Exploiting Fitness Distance Correlation of Set Covering Problems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2279))

Abstract

The set covering problem is an NP-hard combinatorial optimization problem that arises in applications ranging from crew scheduling in airlines to driver scheduling in public mass transport. In this paper we analyze search space characteristics of a widely used set of benchmark instances through an analysis of the fitness-distance correlation. This analysis shows that there exist several classes of set covering instances that show a largely different behavior. For instances with high fitness distance correlation, we propose new ways of generating core problems and analyze the performance of algorithms exploiting these core problems.

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References

  1. E. Housos and T. Elmoth. Automatic optimization of subproblems in scheduling airlines crews. Interfaces, 27(5):68–77, 1997.

    Google Scholar 

  2. H. Lourenço, R. Portugal, and J.P. Paix ao. Multiobjective metaheuristics for the bus-driver scheduling problem. Transportation Science, 35(3):331–343, 2001.

    Article  MATH  Google Scholar 

  3. F.J. Vasko and F.E. Wolf. Optimal selection of ingot sizes via set covering. Operations Research, 15:115–121, 1988.

    MATH  MathSciNet  Google Scholar 

  4. A. Caprara, M. Fischetti, and P. Toth. Algorithms for the set covering problem. Technical Report OR-98-3, DEIS, University of Bologna, Italy, 1998.

    Google Scholar 

  5. S. Ceria, P. Nobili, and A. Sassano. A lagrangian-based heuristic for large-scale set covering problems. Mathematical Programming, 81:215–228, 1998.

    MathSciNet  Google Scholar 

  6. A. Caprara, M. Fischetti, and P. Toth. A heuristic method for the set covering problem. Operations Research, 47:730–743, 1999.

    MATH  MathSciNet  Google Scholar 

  7. J.E. Beasley and P.C. Chu. A genetic algorithm for the set covering problem. European Journal of Operational Research, 94:392–404, 1996.

    Article  MATH  Google Scholar 

  8. A.V. Eremeev. A genetic algorithm with a non-binary representation for the set covering problem. In Proceedings of OR’98, pages 175–181. Springer Verlag, 1999.

    Google Scholar 

  9. M.J. Brusco, L.W. Jacobs, and G.M. Thompson. A morphing procedure to supplement a simulated annealing heuristic for cost-and coverage-corrleated set covering problems. Annals of Operations Research, 86:611–627, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  10. H.R. Lourenço, O. Martin, and T. Stützle. Iterated local search. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics. Kluwer Academic Publishers, Boston, MA, USA, 2002. to appear.

    Google Scholar 

  11. E. Marchiori and A. Steenbeek. An evolutionary algorithm for large scale set covering problems with application to airline crew scheduling. In Real World Applications of Evolutionary Computing, volume 1083 of Lecture Notes in Computer Science, pages 367–381. Springer Verlag, Berlin, Germany, 2000.

    Google Scholar 

  12. M. Kishida M. Yagiura and T. Ibaraki. A 3-flip neighborhood local search for the set covering problem. submitted for publication, 2001.

    Google Scholar 

  13. T. Jones and S. Forrest. Fitness distance correlation as a measure of problem dificulty for genetic algorithms. In L.J. Eshelman, editor, Proc. of the 6th Int. Conf. on Genetic Algorithms, pages 184–192. Morgan Kaufman, 1995.

    Google Scholar 

  14. P.F. Stadler. Towards a theory of landscapes. Technical Report Technical Report SFI-95-03-030, Santa Fe Institute, 1995.

    Google Scholar 

  15. E.D. Weinberger. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics, 63:325–336, 1990.

    Article  MATH  Google Scholar 

  16. K.D. Boese, A.B. Kahng, and S. Muddu. A New Adaptive Multi-Start Technique for Combinatorial Global Optimization. Operations Research Letters, 16:101–113, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  17. E. Balas and M.C. Carrera. A dynamic subgradient-based branch and bound procedure for set covering. Operations Research, 44:875–890, 1996.

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Intellectics Group, Darmstadt University of Technology, Alexanderstr. 10, 64283, Darmstadt, Germany

    Markus Finger & Thomas Stützle

  2. Department of Economics and Business, Universitat Pompeu Fabra, R. Trias Fargas 25–27, 08005, Barcelona, Spain

    Helena Lourenço

Authors
  1. Markus Finger
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  2. Thomas Stützle
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  3. Helena Lourenço
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Editor information

Editors and Affiliations

  1. Dept.of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

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© 2002 Springer-Verlag Berlin Heidelberg

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Finger, M., Stützle, T., Lourenço, H. (2002). Exploiting Fitness Distance Correlation of Set Covering Problems. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_7

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  • DOI: https://doi.org/10.1007/3-540-46004-7_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43432-0

  • Online ISBN: 978-3-540-46004-6

  • eBook Packages: Springer Book Archive

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