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A Comparison of Evolutionary Approaches to the Shortest Common Supersequence Problem

  • Carlos Cotta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

Abstract

The Shortest Common Supersequence problem is a hard combinatorial optimization problem with numerous practical applications. Several evolutionary approaches are proposed for this problem, considering the utilization of penalty functions, GRASP-based decoders, or repairing mechanisms. An empirical comparison is conducted, using an extensive benchmark comprising problem instances of different size and structure. The empirical results indicate that there is no single best approach, and that the size of the alphabet, and the structure of strings are crucial factors for determining performance. Nevertheless, the repair-based EA seems to provide the best performance tradeoff.

References

  1. 1.
    Foulser, D., Li, M., Yang, Q.: Theory and algorithms for plan merging. Artificial Intelligence 57, 143–181 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Timkovsky, V.: Complexity of common subsequence and supersequence problems and related problems. Cybernetics 25, 565–580 (1990)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Hallet, M.: An integrated complexity analysis of problems from computational biology. PhD thesis, University of Victoria (1996)Google Scholar
  4. 4.
    Bodlaender, H., Downey, R., Fellows, M., Wareham, H.: The parameterized complexity of sequence alignment and consensus. Theoretical Computer Science 147, 31–54 (1994)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Middendorf, M.: More on the complexity of common superstring and supersequence problems. Theoretical Computer Science 125, 205–228 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Pietrzak, K.: On the parameterized complexity of the fixed alphabet shortest common supersequence and longest common subsequence problems. Journal of Computer and System Sciences 67, 757–771 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Fraser, C.: Subsequences and Supersequences. PhD thesis, University of Glasgow, Department of Computer Science (1995)Google Scholar
  8. 8.
    Branke, J., Middendorf, M., Schneider, F.: Improved heuristics and a genetic algorithm for finding short supersequences. OR-Spektrum 20, 39–45 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Downey, R., Fellows, M.: Parameterized Complexity. Springer, Heidelberg (1998)zbMATHGoogle Scholar
  10. 10.
    Cotta, C., Troya, J.: A hybrid genetic algorithm for the 0-1 multiple knapsack problem. In: Smith, G., Steele, N., Albrecht, R. (eds.) Artificial Neural Nets and Genetic Algorithms, Wien New York, pp. 251–255. Springer, Heidelberg (1998)Google Scholar
  11. 11.
    Feo, T., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Prais, M., Ribeiro, C.C.: Parameter variation in GRASP procedures. Investigación Operativa 9, 1–20 (2000)Google Scholar
  13. 13.
    Prais, M., Ribeiro, C.: Reactive GRASP: an application to a matrix decomposition problem in TDMA traffic assignment. INFORMS Journal on Computing 12, 164–176 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Cotta, C., Fernández, A.J.: A hybrid GRASP – evolutionary algorithm approach to golomb ruler search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 481–490. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carlos Cotta
    • 1
  1. 1.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaUniversity of MálagaMálagaSpain

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