Selection Strategies for Ambiguous Graph Matching by Evolutionary Optimisation

  • Richard Myers
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


This paper considers how ambiguous graph matching can be realised using a hybrid genetic algorithm. The problem we address is how to maximise the solution yield of the genetic algorithm when the available attributes are ambiguous. We focus on the role of the selection operator. A multi-modal evolutionary optimisation framework is proposed, which is capable of simultaneously producing several good alternative solutions. Unlike other multi-modal genetic algorithms, the one reported here requires no extra parameters: solution yields are maximised by removing bias in the selection step, while optimisation performance is maintained by a local search step.


Genetic Algorithm Selection Operator Hybrid Genetic Algorithm Graph Match Solution Yield 
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.


  1. 1.
    L. G. Shaprio and R. M. Haralick. A metric for comparing relational descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:90–94, 1985.CrossRefGoogle Scholar
  2. 2.
    A. K. C. Wong and M. You. Entropy and distance of random graphs with application to structural pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:599–609, 1985.zbMATHGoogle Scholar
  3. 3.
    J. Kittler, W. J. Christmas, and M. Petrou. Probabilistic relaxation for matching problems in computer vision. In Proceedings of the 4th IEEE International Conference on Computer Vision, pages 666–673, 1993.Google Scholar
  4. 4.
    D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the 2nd International Conference on Genetic Algorithms, pages 41–49, 1987.Google Scholar
  5. 5.
    W. Cedeño, V. R. Vemuri, and T. Slezak. Multiniche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments. Evolutionary Computation, 2:321–345, 1995.CrossRefGoogle Scholar
  6. 6.
    D. Beasley, D. R. Bull, and R. R. Martin. A sequential niche technique for multimodal function optimisation. Evolutionary Computation, 1:101–125, 1993.CrossRefGoogle Scholar
  7. 7.
    M. Gorges-Schleuter. ASPARAGOS: A parallel genetic algorithm for population genetics. In Parallelism, Learning, Evolution. Workshop on Evolutionary Models and Strategies, pages 407–418, 1991.Google Scholar
  8. 8.
    R. E. Smith, S. Forrest, and A. S. Perelson. Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation, 1:127–149, 1993.CrossRefGoogle Scholar
  9. 9.
    A. D. J. Cross, R. C. Wilson, and E. R. Hancock. Inexact graph matching using genetic search. Pattern Recognition, 30:953–970, 1997.CrossRefGoogle Scholar
  10. 10.
    R. C. Wilson and E. R. Hancock. Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:634–648, 1997.CrossRefGoogle Scholar
  11. 11.
    R. Myers, R. C. Wilson, and E. R. Hancock. Efficient relational matching with local edit distance. In Proceedings of the 14 th International Conference on Pattern Recognition, pages 1711–1714, 1998.Google Scholar
  12. 12.
    D. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, 1989.Google Scholar
  13. 13.
    J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In Proceedings of the 2 nd International Conference on Genetic Algorithms, pages 14–21, 1987.Google Scholar
  14. 14.
    D. E. Goldberg. A note on Boltzmann tournament selection for genetic algorithms and population-based simulated annealing. Complex Systems, 4:445–460, 1990.zbMATHGoogle Scholar
  15. 15.
    A. Prügel-Bennett and J. L. Shapiro. An analysis of genetic algorithms using statistical physics. Physical Review Letters, 72:1305–1309, 1994.CrossRefGoogle Scholar
  16. 16.
    I. Rechenberg. Evolutionsstrategie-Optimierung Technischer Systeme nach Prinzipien der biologischen Information. Fromman Verlag, 1973.Google Scholar
  17. 17.
    H-P. Schwefel. Numerical Optimization of Computer Models. Wiley, 1981.Google Scholar
  18. 18.
    G. Rudolph. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5:96–101, 1994.CrossRefGoogle Scholar
  19. 19.
    L. J. Eshelman. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, volume 1, pages 265–283. Morgan Kaufmann, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Richard Myers
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYork

Personalised recommendations