Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{defranca2023interpretablesymbolicregressiondata,

• author = "F. O. {de Franca} and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. {La Cava}",
• title = "Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition",
• howpublished = "arXiv 2304.01117",
• year = "2023",
• month = "3 " # jul,
• note = "v3",
• keywords = "genetic algorithms, genetic programming, Symbolic Regression, Competition, Interpretable Machine Learning, XAI",
• eprint = "2304.01117",
• archiveprefix = "arXiv",
• primaryclass = "cs.LG",
• URL = "https://arxiv.org/abs/2304.01117",
• size = "13 pages",
• abstract = "Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead use approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate this http URL present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions.",
• notes = "submitted to IEEE Transactions on Evolutionary Computation \cite{deFranca:ieeeTEC}

  GECCO-2022",

}

Genetic Programming entries for Fabricio Olivetti de Franca Marco Virgolin Michael Kommenda Maimuna Majumder Miles Cranmer Guilherme Jorge Nunes Monteiro Espada Leon Ingelse Alcides Fonseca Mikel Landajuela Brenden Kyle Petersen Ruben Glatt T Nathan Mundhenk Chak Shing Lee Jacob Dean Hochhalter David L Randall Pierre-Alexandre Kamienny Hengzhe Zhang Grant Dick Alessandro Simon Bogdan Burlacu Jaan Kasak Meera Machado Casper Wilstrup William La Cava

Citations