Parameterizing a genetic optimizer

Authors:
Victor Muntés-Mulero;Marta Pérez-Casany;Josep Aguilar-Saborit;Calisto Zuzarte;Josep-Ll. Larriba-Pey
Affiliations:
Computer Architecture Dept., Universitat Politècnica de Catalunya;Applied Mathematics II Dept., Universitat Politècnica de Catalunya, Barcelona, Spain;Computer Architecture Dept., Universitat Politècnica de Catalunya;IBM Canada Ltd, IBM Toronto Lab., Markham, Ontario, Canada;Computer Architecture Dept., Universitat Politècnica de Catalunya
Venue:
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Year:
2006

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Abstract

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.