Multiview Symbolic Regression

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

@InProceedings{russeil:2024:GECCO,

• author = "Etienne Russeil and Fabricio Olivetti {de Franca} and Konstantin Malanchev and Bogdan Burlacu and Emille Ishida and Marion Leroux and Clement Michelin and Guillaume Moinard and Emmanuel Gangler",
• title = "Multiview Symbolic Regression",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "961--970",
• 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, symbolic regression, interpretability",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654087",
• size = "10 pages",
• abstract = "Symbolic regression (SR) searches for analytical expressions representing the relationship between explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, the researcher is confronted with multiple sets of results obtained from experiments conducted with different set-ups. Traditional SR methods may fail to find the underlying expression since the parameters of each experiment can be different. In this work we present Multiview Symbolic Regression (MvSR), which takes into account multiple datasets simultaneously, mimicking experimental environments, and outputs a general parametric solution. This approach fits the evaluated expression to each independent dataset and returns a parametric family of functions f(x; Theta) simultaneously capable of accurately fitting all datasets. We demonstrate the effectiveness of MvSR using data generated from known expressions, as well as real-world data from astronomy, chemistry and economy, for which an a priori analytical expression is not available. Results show that MvSR obtains the correct expression more frequently and is robust to hyperparameters change. In real-world data, it is able to grasp the group behaviour, recovering known expressions from the literature as well as promising alternatives, thus enabling the use MvSR to a large range of experimental scenarios.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Etienne Russeil Fabricio Olivetti de Franca Konstantin Malanchev Bogdan Burlacu Emille Ishida Marion Leroux Clement Michelin Guillaume Moinard Emmanuel Gangler

Citations