Parse-matrix evolution for symbolic regression

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@Article{Luo2012,

• author = "Changtong Luo and Shao-Liang Zhang",
• title = "Parse-matrix evolution for symbolic regression",
• journal = "Engineering Applications of Artificial Intelligence",
• year = "2012",
• volume = "25",
• number = "6",
• pages = "1182--1193",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2012.05.015",
• URL = "http://www.sciencedirect.com/science/article/pii/S0952197612001212",
• keywords = "genetic algorithms, genetic programming, Data analysis, Symbolic regression, Grammatical evolution, Artificial intelligence, Evolutionary computation",
• abstract = "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.",

}

Genetic Programming entries for Changtong Luo Shao-Liang Zhang

Citations