Skip to main content

A Memetic Algorithm Guided by Quicksortfor the Error-Correcting Graph Isomorphism Problem

  • Conference paper
  • First Online:
Applications of Evolutionary Computing (EvoWorkshops 2002)

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

Included in the following conference series:

  • 712 Accesses

Abstract

Sorting algorithms define paths in the search space of n! permutations based on the information provided by a comparison predicate. We guide a Memetic Algorithm with a new mutation operator. Our mutation operator performs local search following the path traced by the Quicksort mechanism. The comparison predicate and the evaluation function are made to correspond and guide the evolutionary search. Our approach improves previous results for a benchmark of experiments of the Error-Correcting Graph Isomorphism. For this case study, our new Memetic Algorithm achieves a better quality vs effort trade-off and remains highly effective even when the size of the problem grows.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Greffenstette, J.: Incorporating problem specific knowledge into genetic algorithms. Davis, L., ed.: Genetic Algorithms and Simulated Annealing, Pitman (1987) 42–60

    Google Scholar 

  2. Mühlenbein, H.: Parallel genetic algorithms, population genetics and combinatorial optimization. Schaffer, J., ed.: Proc. 3rd Int. Conf. Genetic Algorithms, George Mason Univ., Morgan Kaufmann (1989) 416–421

    Google Scholar 

  3. Davis, L., ed.: Handbook of Genetic Algorithms. Van Nostrand Reinhold (1991)

    Google Scholar 

  4. Mühlenbein, H.: Evolution in time and space-the parallel genetic algorithm. Rawlins, G., ed.: Foundations of Genetic Algorithms, Indiana Univ., Morgan Kaufmann (1991) 316–337

    Google Scholar 

  5. Merz, P., Freisleben, B.: A genetic local search approach to the quadratic assignment problem. Bäck, T., ed.: Proc. 7th Int. Conf. Genetic Algorithms, Michigan State Univ., East Lansing, Morgan Kaufmann (1997) 465–472

    Google Scholar 

  6. Merz, P., Freisleben, B.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE T. Evolutionary Computation 4 (2000) 337–352

    Article  Google Scholar 

  7. Tsai, H.K., Yang, J.M., Kao, C.Y.: A genetic algorithm for traveling salesman problems. Spector, L., et al. eds.: GECCO-2001. Proc. Genetic and Evolutionary Conference, San Francisco, CA. Morgan Kaufmann (2001) 687–693

    Google Scholar 

  8. Rocha, M., Mendes, R., Cortez, P., Neves, J.: Sitting guests at a wedding party: Experiments on genetic and evolutionary constrained optimization. Congress on Evolutionary Computation CEC2001, Seoul, Korea, IEEE Press (2001) 671–678

    Google Scholar 

  9. Estivill-Castro, V., Torres-Velázquez, R.: Classical sorting embedded in genetic algorithms for improved permutation search. Congress on Evolutionary Computation CEC2001, Seoul, Korea, IEEE Press (2001) 941–948

    Google Scholar 

  10. Estivill-Castro, V., Torres-Velázquez, R.: How should feasibility be handled by genetic algorithms on constraint combinatorial optimization problems? the case of the valued n-queens problem. 2nd Workshop on Memetic Algorithms. WOMA II. GECCO-2001. (2001) 146–151

    Google Scholar 

  11. Wang, Y.K., Fan, K.C., Horng, J.T.: Genetic-based search for error-correcting graph isomorphism. IEEE T. Systems, Man and Cybernetics, Part B: Cybernetics 27 (1997) 588–597

    Article  Google Scholar 

  12. Tsai, W.H., Fu, K.S.: Error-correcting isomorphisms of attributed relational graphs for pattern analysis. IEEE T. Systems, Man and Cybernetics 9 (1979) 757–768

    Article  MATH  Google Scholar 

  13. Messmer, B., Bunke, H.: A decision tree approach to graph and subgraph isomorphism detection. Pattern Recognition (1999) 1979–1998

    Google Scholar 

  14. Aarts, E., Lenstra, J.: Introduction. Aarts, E., Lenstra, J., eds.: Local Search in Combinatorial Optimization, Wiley (1997) 1–17

    Google Scholar 

  15. Knuth, D.: Sorting and Searching. Volume 3 of The Art of Computer Programming. Addison-Wesley (1973)

    Google Scholar 

  16. Sedgewick, R.: Algorithms in C++. Addison-Wesley (1992)

    Google Scholar 

  17. Estivill-Castro, V., Wood, D.: Randomized adaptive sorting. Random Structures and Algorithms 4 (1993) 26–51

    MathSciNet  Google Scholar 

  18. Croes, G.: A method for solving traveling-salesman problems. Operations Research 5 (1958) 791–812

    Article  MathSciNet  Google Scholar 

  19. Goldberg, D., Lingle, R.J.: Alleles, loci, and the traveling salesman problem. Grefenstette, J., ed.: Proc. Int. Conf. Genetic Algorithms and their Applications, Carnegie Mellon Univ., Lawrence Erlbaum (1985) 154–159

    Google Scholar 

  20. Baker, J.: Adaptive selection methods for genetic algorithms. Grefenstette, J., ed.: Proc. Int. Conf. on Genetic Algorithms and their Applications, Carnegie Mellon Univ., Lawrence Erlbaum (1985)

    Google Scholar 

  21. Wolpert, D.H., MacReady, W.: No free lunch theorems for optimization. IEEE T. on Evolutionary Computation 1 (1997) 67–82

    Article  Google Scholar 

  22. Reingold, E., Nievergelt, J., Deo, N.: Combinatorial Algorithms, Theory and Practice. Prentice-Hall, Englewood Cliffs, NJ (1977)

    Google Scholar 

  23. Li, M., Vitanyi, P.: A theory of learning simple concepts under simple distributions and average case complexity for the universal distribution. Proc. 30th IEEE Symp. on Foundations of Computer Science, Research Triangle Park, NC. (1989) 34–39

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Torres-Velázquez, R., Estivill-Castro, V. (2002). A Memetic Algorithm Guided by Quicksortfor the Error-Correcting Graph Isomorphism Problem. 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_18

Download citation

  • DOI: https://doi.org/10.1007/3-540-46004-7_18

  • 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

Publish with us

Policies and ethics