作者：杨兰菊, 刘齐宏, 林建强

作者单位：四川大学电气信息学院, 四川成都 610065

关键词：GEP; 多元线性回归; 实测数据; 预测函数关系; 实验结果比较

摘要：

基因表达式编程(GEP)是一种基于基因组和表现型组的新遗传算法,是一种高度有效、稳定的随机搜索方法,能从大量的数据集中挖掘出未知的、有价值的函数模型。本文根据各种燃料成本与用电成本的实际数据,提出用基因表达式编程(GEP)对其预测,挖掘出它们的函数关系式,并和多元线性回归预测结果进行比较。实验结果发现,用GEP方法避免了事先确定变量之间函数关系的主观性、经验性、预估性,从而使预测效果更加客观、有效。

Gene expression programming in prediction of multiple variabes

YANG Lan-ju, LIU Qi-hong, LIN Jian-qiang

School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China

Abstract: In this paper,GEP,a genotype/phenotype genetic algorithm,is applied to solve the prediction of multiple variables and it can mine the pre-unkonwn and valuable function model.According to the data cases of practical problem between the costs of fuels and the cost of spending electricity,GEP mines the function express between data input and data output,and it compares with the multiple variables linear regression in terms of accuracy and efficiency.Experiments show that GEP has better predicative results than the multiple variables linear regression,the output values of prediction using GEP algorithms are more near the actual values.So GEP is an efficient search method in prediction.

Keywords: GEP; Multiple variables linear regression; Actual data cases; Prediction of function express; Comparison of experiment results

2007, 33(2): 124-127  收稿日期: 2006-9-29;收到修改稿日期: 2006-11-15

基金项目: 四川省科技攻关项目(2006Z01-027)

作者简介: 杨兰菊(1975-),女,四川成都市人,硕士研究生,研究方向:计算机控制与管理。

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