Research On Query Rewriting Optimization Based On Improved Genetic Algorithm In E-commerce System

In recent decades,the digital economy has developed rapidly in China,and the Internet business has experienced explosive growth in the past decade and is expected to continue to grow.Due to the rapid development of the Internet and mobile infrastructure,information technology has also increased rapidly,resulting in the increasing popularity of e-commerce systems and the increasing frequency of commercial transactions.People have entered the information society and the era of big data,and the various data of human society have grown exponentially.Therefore,under the premise of massive data,the problem of fast and accurate query of e-commerce system has been paid more and more attention.The query optimization technology of the current e-commerce system has been widely used,such as dynamic materialized view technology,cache technology,etc.,but since the data of the e-commerce system is massive and updated in real time,some query optimization techniques are not effective.The query rewrite optimization algorithm is increasingly being valued by current scholars.At present,commonly used database query rewriting optimization algorithms include aenetic algorithms,dynamic programming algorithms,greedy algorithms.and ant colony aloorithms.Due to the robustness,strong adaptability,implicit parallelism and powerful search ability of genetic algorithm,this paper proposes a research on query rewriting optimization of e-commerce system based on improved genetic alsorithm.Since the traditional genetic algorithm is easy to fall into the local minimum problem and the local search efficiency is poor,the followingt wo improvements are made in the traditional genetic algorithm:the selection of the firetly alsorithm is improved by the firefly algorithm in the selection strategy.Each time an individual is selected to enter the progeny population,the probability of occurrence of each individual in the parent population in the offspring is recalculated.ensuring that the relative probability of the individual with greater fitness is greater in the paternal population,improving the roulette selection.The shortcomings of algorithm selection error are large.Then the implementation principles of crossover operator and mutation operator are introduced in detail.Before the crossover process and mutation process,the initial population is divided into three populations according to the fitness degree,then The three populations set different crossover rates and mutation rates,allowing individuals with large fitness to perform cross-variation operations as little as possible,thereby ensuring the superiority of the offspring population.Finally,several experiments are carried out in combination with the e-commerce system built in this paper.The experimental results show that the improved genetic algorithm is shorter than the traditional genetic algorithm.