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Genetic algorithm optimization of distributed database queries

conference contribution
posted on 06.06.2019, 00:00 by Michael GregoryMichael Gregory
Distributed relational database query optimisation is a combinatorial optimisation problem. This paper reports on an initial investigation into the potential for a genetic algorithm (GA) to optimism distributed queries. A genetic algorithm is developed and its performance compared with alternative stochastic optimisation techniques: random search, multistart, and simulated annealing. The problem of fully reducing all tables in a tree query is used to compare the techniques. For this problem, evaluating the fitness function is an expensive operation. The proposed GA uses a tree-structured data model with tailored crossover and mutation operators that avoid the need to fully re-evaluate the fitness function for new solutions. Query optimisation is a task that must be performed in real-time. A technique is required that performs well at the start of a search, but avoids the problem of premature convergence. The proposed GA uses a local search phase to deliver the required real-time performance. Experiments show that the proposed GA can perform better than the alternative techniques, tested. The potential for a GA to deliver valuable distributed query processing cost reductions is demonstrated.

History

Parent Title

1998 IEEE International Conference on Evolutionary Computation Proceedings

Start Page

271

End Page

276

Number of Pages

6

Start Date

04/05/1998

Finish Date

09/05/1998

ISBN-10

0780348699

Location

Anchorage, AK, USA

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

IEEEWorld Congress on Computational Intelligence (WCCI '98)