计算机应用 ›› 2012, Vol. 32 ›› Issue (11): 3050-3053.DOI: 10.3724/SP.J.1087.2012.03050

• 人工智能 • 上一篇    下一篇

基于均匀设计抽样的改进遗传算法在回归模型中的应用

施明华1,周本达1,2,陈明华1   

  1. 1. 皖西学院 应用数学学院,安徽 六安 237012
    2. 安徽省自然计算及其应用重点实验室,合肥 230037
  • 收稿日期:2012-05-18 修回日期:2012-07-03 发布日期:2012-11-12 出版日期:2012-11-01
  • 通讯作者: 施明华
  • 作者简介:施明华(1984-),男,安徽六安人,讲师,硕士,主要研究方向:统计计算;周本达(1974-),男,安徽六安人,副教授,主要研究方向:智能算法、生物信息学;陈明华(1956-),男,浙江鄞县人,教授,主要研究方向:大样本统计理论。
  • 基金资助:
    国家自然科学基金资助项目(61175051,61070131);安徽省高校优秀人才基金资助项目(2009SQRZ189);安徽省高校省级自然科学研究重点项目(K2011A267)

Application of improved genetic algorithm based on uniform design sampling crossover operator in regression model

SHI Ming-hua1,ZHOU Ben-da1,2,ZHOU Ming-hua3   

  1. 1. College of Applied Mathematics, West Anhui University, Lu’an Anhui 237012,China
    2. Anhui Provincial Nature Inspired Computation and Applications Laboratory, Hefei Anhui 230037,China
    3. Department of Mathematics and Physics, West Anhui University, Lu'an Anhui 237012, China
  • Received:2012-05-18 Revised:2012-07-03 Online:2012-11-12 Published:2012-11-01
  • Contact: SHI Ming-hua

摘要: 通过对佳点集遗传算法优缺点进行分析,利用均匀设计抽样(UDS)的理论和方法,对遗传算法中的交叉操作进行重新设计,提出一种改进的遗传算法。新算法将变量选择和变换选择并行实施,并结合统计信息准则处理回归模型选择问题。仿真实验表明新算法在求解精度、解的稳定性等方面有较大的提高。

关键词: 回归模型选择, 遗传算法, 均匀设计抽样

Abstract: After analyzing the advantages and disadvantages of good point genetic algorithm, the crossover operation of Genetic Algorithm (GA) was redesigned by using the theory and methods of Uniform Design Sampling (UDS). Then an improved GA based on UDS was presented. In combination with statistical criteria, the new algorithm was used for variable and transformation simultaneous selection in solving regression model selection problem. The results of simulation show a good improvement in solution quality, stability and other various indices.

Key words: Regression model selection, Genetic Algorithms(GA), Uniform Design Sampling (UDS) Regression model selection, Genetic Algorithms(GA), Uniform Design Sampling (UDS)

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