Optimization of large join queries
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Randomized algorithms for optimizing large join queries
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
Genetic programming in database query optimization
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Generating query plans for distributed query processing using genetic algorithm
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
An evolutionary multi-agent system for database query optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Distributed Query Plan Generation using Particle Swarm Optimization
International Journal of Swarm Intelligence Research
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Genetic algorithms (GAs) have long been used for large join query optimization (LJQO). Previous work takes all queries as based on one granularity to optimize GAs and compares their efficiency with other query optimization algorithms. However, we believe that large join queries are based on a granularity that is too large (1) to optimize GAs and (2) to compare the efficiency of different randomized optimization algorithms. Besides, while previous work only discusses the efficiency of basic GAs for LJQO, we believe that hybrid GAs reduce search space to improve GAs efficiency. We will present a genetic optimization model which includes factors affecting the efficiency of GAs. In this model, the query model is the granularity upon which GAs are optimized. Based on six typical query models, experiments have been done, first, to optimize four classes of GAs; and second, to prove the rationality of the query model as a trade-off between the efficiency and robustness of GAs. Finally, we will provide suggestions for choosing one of four classes of GAs and for the settings and combinations of components of GAs.