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Finding Diverse Examples Using Genetic Algorithms

  • Colin G. Johnson
Part of the Advances in Soft Computing book series (AINSC, volume 9)

Abstract

The problem of finding qualitative examples is an interesting yet little studied machine learning problem. Take a set of objects, O and a set of classes C, where each object fits into one and only one class. Represent this classification by a total function f: O C. We assume that ∣range(f)∣ « ∣O∣.

Keywords

Test Problem Random String Royal Road Traditional Genetic Algorithm Multimodal Optimization 
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.

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References

  1. Adams, C. A. (1994). The knot book. W.H. Freeman.Google Scholar
  2. Attwood, T., and Parry-Smith, D. (1999). Introduction to bioinformatics. Addison Wesley Longman.Google Scholar
  3. Bäck, T. (1996). Evolutionary algorithms in theory and practice. Oxford University Press.Google Scholar
  4. Belew, R. K. (2000). Finding out about: information retrieval and other technologies for seeking knowledge. Cambridge University Press. (In preparation)Google Scholar
  5. Bentley, P. J. (Ed.). (1999). Evolutionary design by computers. Academic Press.Google Scholar
  6. Boden, M. (1990). The creative mind: Myths and mechanisms. Abacus.Google Scholar
  7. De Jong, K. (1993). Genetic algorithms are NOT function optimizers. In L. Whitley (Ed.), Foundations of genetic algorithms 2 (pp. 5–17). Morgan KauffmannGoogle Scholar
  8. Goldberg, D. E. (1987). Simple genetic algorithms and the minimal deceptive problem. In L. D. Davis (Ed.), Genetic algorithms and simulated annealing. Morgan Kaufmann.Google Scholar
  9. Harvey, I. (1997). Cognition is not computation: Evolution is not optimisation. In W. Gerstner, A. Germond, M. Hasler,, and J.-D. Nicoud (Eds.), Proceedings of the seventh international conference on artificial neural networks (pp. 685–690). Springer-Verlag.Google Scholar
  10. Holland, J. H. (1975). Adaptation in natural and artificial systems. MIT Press. (Second edition 1992)Google Scholar
  11. Hoste, J., Thistlethwaite, M., and Weeks, J. (1998). The first 1,701,936 knots. The Mathematical Intelligencer, 20(4), 33–48.Google Scholar
  12. Johnson, C. G. (2000). Understanding complex systems through examples: a framework for qualitative example finding. In P. A. Gelepithis (Ed.), Complex intelligent systems. Kingston University.Google Scholar
  13. Mafoud, S. W. (1997). Niching methods. In T. Bäck, D. B. Fogel, and Z. Michalewicz (Eds.), Handbook of evolutionary computation (pp. C6.1.1—C6.1.4). Oxford University Press/Institute of Physics.Google Scholar
  14. Mitchell, M. (1996). An introduction to genetic algorithms. Bradford Books/MIT Press.Google Scholar
  15. Mitchell, M., Forrest, S., and Holland, J. (1992). The royal road for genetic algorithms: Fitness landscapes and GA performance. In F. Varela and P. Bourgine (Eds.), Towards a practice of autonomous systems: Proceedings of the first european conference on artificial life. MIT Press. Murasugi, K. (1996). Knot theory and its applications. Birkhäuser.Google Scholar
  16. Partridge, D., and Rowe, J. (1994). Computers and creativity. Intellect Books. van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworths.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Colin G. Johnson
    • 1
  1. 1.Computing LaboratoryUniversity of Kent at CanterburyCanterbury, KentEngland

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