Multi-Join Query Optimization in Distributed Database Based on Genetic Algorithm

As the data capacity extending of the distributed database, the problem of multi-join query optimization largely influence the efficiency of the data queries. The main content of this thesis is to improve the genetic algorithm based on coded tree. The thesis put forward a new mutation operator, which can solve the problem that crossover operator's capability of generating new offspring is not better. Results obtained by the author's experiment show that we get a set of appropriate values of genetic algorithm's parameters, and use the values to process multi-join queries. Simulator confirmed the improved algorithm is more efficient for query optimization than before.