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.

Keywords: genetic algorithms, query optimization, join ordering, randomized algorithms

ACM Classification Keywords: H.2.4. Query processing, H.3.4. Performance evaluation (efficiency and effectiveness)

Link:

AN ADAPTIVE GENETIC ALGORITHM WITH DYNAMIC POPULATION SIZE FOR OPTIMIZING JOIN QUERIES

Stoyan Vellev

http://www.foibg.com/ibs_isc/ibs-02/IBS-02-p11.pdf