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Genetic algorithms and the search for optimal database index selection

  • Farshad Fotouhi
  • Carlos E. Galarce
Track 7: Data Base
Part of the Lecture Notes in Computer Science book series (LNCS, volume 507)

Abstract

The problem of the search for an optimum database index selection problem is an NP-complete problem. Genetic algorithms have been shown to be robust algorithms for searching large spaces for optimal objective function values. Genetic algorithms use historical information to speculate about new areas in the search space with expected improved performance. The feasibility of the application of genetic algorithms to the optimal database index selection is studied in this paper.

Keywords

Genetic Algorithm Adaptive System Travel Salesman Problem Crossover Operator Index Selection 
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. [Booker 87]
    Booker, L.B., Goldberg, D.E., Holland, J.H., Classifier Systems and Genetic Algorithms. Cognitive Science and Machine Learning Laboratory, Technical Report No. 8. The University of Michigan, Ann Arbor, MI.Google Scholar
  2. [Comer 78]
    Comer, D. The difficulty of optimum index selection. ACM Transactions on Database Systems, 3(4), 440–445.Google Scholar
  3. [Goldberg 89]
    Goldberg, D.E. Genetic Algorithms: In search optimization & machine learning. Reading, MA: Addison-Wesley.Google Scholar
  4. [Grefenstette 85]
    Grefenstette, J.J., Gopala, B.J., & D.V. Gucht, Genetic Algorithms for the Traveling Salesman Problem. In Proceedings of an International Conference on Genetic Algorithms and Their Applications (pp 136–140), Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
  5. [Holland 75]
    Holland, J.H. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI.Google Scholar
  6. [Holland 86]
    Holland, J.H. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J.G. Carbonell, & T. M. Mitchell (Eds.), Machine Learning II (pp. 593–623). Los Altos, CA: Morgan Kaufmann.Google Scholar
  7. [Piatetsky 83]
    Piatetsky-Shapiro, G. The Optimal Selection of Secondary Indices is NP-Complete. In SIGMOD Record, Vol 13-N2, (pp 72–75).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Farshad Fotouhi
    • 1
  • Carlos E. Galarce
    • 1
  1. 1.Computer Science DepartmentWayne State UniversityDetroitUSA

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