DoME: A deterministic technique for equation development and Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{RIVERO:2022:eswa,

• author = "Daniel Rivero and Enrique Fernandez-Blanco and Alejandro Pazos",
• title = "{DoME:} A deterministic technique for equation development and Symbolic Regression",
• journal = "Expert Systems with Applications",
• volume = "198",
• pages = "116712",
• year = "2022",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2022.116712",
• URL = "https://www.sciencedirect.com/science/article/pii/S0957417422001889",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Machine learning, Artificial intelligence",
• 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