The invention relates to a
query optimization method based on an improved
genetic algorithm, and belongs to the technical field of
query optimization. The method comprises: establishing a
mathematical model for a query execution policy set, to transform a
query optimization problem into a mathematical problem of finding a
global optimal solution, wherein the model is called a
cost evaluation model of a query policy; and then improving a
genetic algorithm, and using a global search ability of the improved
genetic algorithm to perform a
parallel search on a query policy set, thereby finally obtaining an ideal query execution policy. According to the method provided by the invention, a conventional genetic
algorithm is improved, and the improved genetic
algorithm is used for query optimization of a large
relational database, thereby overcoming a "premature" convergence phenomenon. Compared with other intelligent optimization algorithms, falling into a local extremum can be effectively avoided, thereby shortening search time. In addition, a
gene based search policy and a polyploidy based retention policy in the
algorithm greatly improve search accuracy.