Parse-matrix evolution for symbolic regression

被引：28
作者：
Luo, Changtong [1 ]
Zhang, Shao-Liang [2 ]
机构：
[1] Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China
[2] Nagoya Univ, Dept Computat Sci & Engn, Nagoya, Aichi 4648603, Japan
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2012年 / 25卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Genetic programming; Data analysis; Symbolic regression; Grammatical evolution; Artificial intelligence; Evolutionary computation; NONLINEAR-SYSTEMS;
D O I：
10.1016/j.engappai.2012.05.015
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Data-driven model is highly desirable for industrial data analysis in case the experimental model structure is unknown or wrong, or the concerned system has changed. Symbolic regression is a useful method to construct the data-driven model (regression equation). Existing algorithms for symbolic regression such as genetic programming and grammatical evolution are difficult to use due to their special target programming language (i.e., LISP) or additional function parsing process. In this paper, a new evolutionary algorithm, parse-matrix evolution (PME), for symbolic regression is proposed. A chromosome in PME is a parse-matrix with integer entries. The mapping process from the chromosome to the regression equation is based on a mapping table. PME can easily be implemented in any programming language and free to control. Furthermore, it does not need any additional function parsing process. Numerical results show that PME can solve the symbolic regression problems effectively. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1182 / 1193
页数：12
相关论文
共 27 条
  • [1] [Anonymous], 2003, Genetic programming IV: routine human-competitive machine intelligence
  • [2] A ComDarison of Selection Schemes Used in Evolutionary Algorithms
    Blickle, Tobias
    Thiele, Lothar
    [J]. EVOLUTIONARY COMPUTATION, 1996, 4 (04) : 361 - 394
  • [3] Brabazon A, 2006, INFORM-J COMPUT INFO, V30, P325
  • [4] Brameier Markus, 2007, Linear genetic programming, V1
  • [5] Chatterjee S., 2006, Regression Analysis by Example, V4th, P317
  • [6] Fang K.-T., 1994, Number-theoretic methods in statistics
  • [7] Functional programming languages
    Goldberg, B
    [J]. ACM COMPUTING SURVEYS, 1996, 28 (01) : 249 - 251
  • [8] Hartl D.L., 2008, GENETICS ANAL GENES
  • [9] Keijzer M, 2003, LECT NOTES COMPUT SC, V2610, P70
  • [10] Luo C., 2012, OPTIMIZATION METHODS
123