Interactive Symbolic Regression - A Study on Noise Sensitivity and Extrapolation Accuracy

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

@InProceedings{raghav:2024:GECCOcomp,

• author = "S. Sanjith Raghav and S. Tejesh Kumar and Rishiikesh Balaji and M. Sanjay and C. Shunmuga Velayutham",
• title = "Interactive Symbolic Regression - A Study on Noise Sensitivity and Extrapolation Accuracy",
• booktitle = "Symbolic Regression",
• year = "2024",
• editor = "William {La Cava} and Steven Gustafson",
• pages = "2076--2082",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, interactive symbolic regression, i-gplearn, gplearn, python, user-interactive search and interactive evolutionary computation",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664130",
• size = "7 pages",
• abstract = "This paper presents an interactive symbolic regression framework i-gplearn, which extends the popular Python Symbolic Regression library gplearn with user interactivity. Predominatly, all the Symbolic Regression (SR) algorithms focus on best model fit despite the fact that the final objective is more about scientific insight for the user. Consequently, the possibility of relying on human intuition and expertise for exploratory discovery of models has greater potential in the discovery of relevant and useful mathematical expressions. i-gplearn combines both user score and gplearn's model accuracy. Recent benchmark studies have demonstrated that the state-of-the-art SR approaches find difficulty in solving problems sensitive to noise and problems demanding extrapolation accuracy. i-gplearn interactive experiments with 11 users have been demonstrated on the two tasks - extrapolation accuracy and sensitive to noise conducted as part of the Symbolic Regression competition hosted in 2022 Genetic and Evolutionary Computation Conference (GECCO). The user studies showed competitive performance of i-gplearn against the baseline gplearn in the sensitive to noise task. In the case of extrapolation accuracy, the user interaction studies showed both huge potential in identifying diverse expressions as well as the challenges of interactive experiments.",
• notes = "GECCO-2024 SymReg A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for S Sanjith Raghav S Tejesh Kumar Rishiikesh Balaji M Sanjay C Shunmuga Velayutham

Citations