Genetic Algorithms for Large Join Query Optimization

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.