Parse-matrix evolution for symbolic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Luo2012,
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author = "Changtong Luo and Shao-Liang Zhang",
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title = "Parse-matrix evolution for symbolic regression",
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journal = "Engineering Applications of Artificial Intelligence",
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year = "2012",
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volume = "25",
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number = "6",
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pages = "1182--1193",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2012.05.015",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197612001212",
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keywords = "genetic algorithms, genetic programming, Data
analysis, Symbolic regression, Grammatical evolution,
Artificial intelligence, Evolutionary computation",
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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