IMI-BAS
 

BulDML at Institute of Mathematics and Informatics >
ITHEA >
International Book Series Information Science and Computing >
2008 >
Book 2 Advanced Research in Artificial Intelligence >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1034

Title: An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries
Authors: Vellev, Stoyan
Keywords: Genetic Algorithms
Query Optimization
Join Ordering
Randomized Algorithms
Query Processing
Issue Date: 2008
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches.
URI: http://hdl.handle.net/10525/1034
ISSN: 1313-0455
Appears in Collections:Book 2 Advanced Research in Artificial Intelligence

Files in This Item:

File Description SizeFormat
IBS-02-p11.pdf127.33 kBAdobe PDFView/Open

 



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2009  The DSpace Foundation - Feedback