An Improved Approach for Materialized View Selection Based on Genetic Algorithm

This paper presents an improved genetic algorithm to solve the materialized view selection problem under query cost constraints. The algorithm dynamically changes the crossover probability and mutation probability in the process of genetic. In this way, it can not only maintain the population diversity, but also ensure the convergence of the genetic algorithm. So it effectively improves the optimization ability of genetic algorithm, thus avoiding the "evolutionary stagnation" problems. Meanwhile, the improved genetic algorithm increases the processing of invalid solution to avoid the "evolutionary stagnation" problems generated by invalid cycle, thereby the efficiency of materialized view selection is greatly