2019/08/31 by Daniel Rivero, Enrique Fernández-Blanco, Enrique Fernandez-Blanco +1
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Artificial intelligence #Computer science #Evolutionary Algorithms and Applications #Expression (computer science) #Genetic programming #Machine learning #Mathematical optimization #Mathematical theory #Mathematics #Metaheuristic Optimization Algorithms Research #Population #Programming language #Regression #Regression analysis #Simple (philosophy) #Statistics #Symbolic regression #Theoretical computer science #cs.LG #stat.ML
paper · pdf · doi:10.1016/j.eswa.2022.116712
published as Expert Systems with Applications, Volume 198, 15 July 2022, 116712 · 29 pages. Work in progress
arxiv created 2019/11/15 · openalex publication_date 2022/03/04 · arxiv updated 2022/03/22 · openalex created_date 2022/04/03 · openalex updated_date 2026/08/01
This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is deterministic, and does not involve the creation of a population of initial solutions. Instead of it, a simple expression is being grown until it fits the data. The experiments performed show that the results are as good as other Machine Learning methods, in a very low computational time. Another advantage of this technique is that the complexity of the expressions can be limited, so the system can return mathematical expressions that can be easily analysed by the user, in opposition to other techniques like GSGP.