DoME: A deterministic technique for equation development and Symbolic Regression
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- @Article{RIVERO:2022:eswa,
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author = "Daniel Rivero and Enrique Fernandez-Blanco and
Alejandro Pazos",
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title = "{DoME:} A deterministic technique for equation
development and Symbolic Regression",
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journal = "Expert Systems with Applications",
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volume = "198",
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pages = "116712",
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year = "2022",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2022.116712",
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URL = "https://www.sciencedirect.com/science/article/pii/S0957417422001889",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Machine learning, Artificial intelligence",
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abstract = "Based on a solid mathematical background, this paper
proposes a method for Symbolic Regression that enables
the extraction of mathematical expressions from a
dataset. Contrary to other approaches, such as Genetic
Programming, the proposed method is deterministic and,
consequently, does not require the creation of a
population of initial solutions. Instead, a simple
expression is grown until it fits the data. This method
has been compared with four well-known Symbolic
Regression techniques with a large number of datasets.
As a result, on average, the proposed method returns
better performance than the other techniques, with the
advantage of returning mathematical expressions that
can be easily used by different systems. Additionally,
this method makes it possible to establish a threshold
at the complexity of the expressions generated, i.e.,
the system can return mathematical expressions that are
easily analyzed by the user, as opposed to other
techniques that return very large expressions",
- }
Genetic Programming entries for
Daniel Rivero Cebrian
Enrique Fernandez-Blanco
Alejandro Pazos Sierra
Citations