Parameterizing a Genetic Optimizer

  • Victor Muntés-Mulero
  • Marta Pérez-Casany
  • Josep Aguilar-Saborit
  • Calisto Zuzarte
  • Josep-Ll. Larriba-Pey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


Genetic programming has been proposed as a possible although still intriguing approach for query optimization. There exist two main aspects which are still unclear and need further investigation, namely, the quality of the results and the speed to converge to an optimum solution. In this paper we tackle the first aspect and present and validate a statistical model that, for the first time in the literature, lets us state that the average cost of the best query execution plan (QEP) obtained by a genetic optimizer is predictable. Also, it allows us to analyze the parameters that are most important in order to obtain the best possible costed QEP. As a consequence of this analysis, we demonstrate that it is possible to extract general rules in order to parameterize a genetic optimizer independently from the random effects of the initial population.


Initial Population Average Cost Mutation Operation Crossover Operation Database Schema 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Victor Muntés-Mulero
    • 1
  • Marta Pérez-Casany
    • 2
  • Josep Aguilar-Saborit
    • 1
  • Calisto Zuzarte
    • 3
  • Josep-Ll. Larriba-Pey
    • 1
  1. 1.Computer Architecture Dept.Universitat Politècnica de Catalunya 
  2. 2.Applied Mathematics II Dept.Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.IBM Canada Ltd, IBM Toronto Lab.MarkhamCanada

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