遗传算法在高校图书馆信息流通量预测中的应用

Application of the Genetic Algorithms in the Universities Library Information Flow Prediction

本文结合遗传算法具有全局最优性和并行性特点,利用遗传算法多变量MGM(1,n)模型参数进行优化,构建了基于遗传算法的MGM(1,n,q)模型.以吉林省某高校图书馆流通部统计1996-2008年的图书流通信息量为例,对模型进行了验证.结果表明,基于遗传算法MGM(1,n,q)模型优于MGM(1,n)模型;MGM(1,n)模型优于GM(1,1)模型.

In this paper,genetic algorithm and parallel global optimization characteristics of genetic algorithm for multi-variable MGM（1,n） model parameters were optimized,constructed based on genetic algorithm MGM（1,n,q） model.To a college library circulation department statistics from 1996 to 2008,circulation of books the amount of information,for example,to validate the model results show that genetic algorithm based on MGM（1,n,q） model excellent MGM（1,n） in the model,MGM（1,n） models is better than GM（1,1） models.