- author = "Fabricio {Olivetti de Franca} and Gabriel Kronberger",
- title = "{rEGGression:} an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models",
- booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
- year = "2025",
- editor = "Carola Doerr and Mike Preuss",
- pages = "4--12",
- address = "Malaga, Spain",
- series = "GECCO '25",
- month = "14-18 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Benchmarking, Benchmarks, Software, Reproducibility, Symbolic regression, Equality saturation, egg, e-graphs, evaluation cache",
- isbn13 = "979-8-4007-1465-8",
-
URL = "
https://doi.org/10.1145/3712256.3726385",
-
DOI = "
doi:10.1145/3712256.3726385",
-
code_url = "https://egraphs-good.github.io/",
- size = "9 pages",
- abstract = "Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linear regression models, delivering a set of hypotheses that balances accuracy with the possibility to understand the phenomena. Many SR implementations return a Pareto front allowing the choice of the best trade-off. However, this hides alternatives that are close to non-domination, limiting these choices. Equality graphs (e-graphs) allow to represent large sets of expressions compactly by efficiently handling duplicated parts occurring in multiple expressions. The e-graphs allow to efficiently store and query all solution candidates visited in one or multiple runs of different algorithms and open the possibility to analyze much larger sets of SR solution candidates. We introduce rEGGression, a tool using e-graphs to enable the exploration of a large set of symbolic expressions which provides querying, filtering, and pattern matching features creating an interactive experience to gain insights about SR models. The main highlight is its focus in the exploration of the building blocks found during the search that can help the experts to find insights about the studied phenomena. This is possible by exploiting the pattern matching capability of the e-graph data structure.",
-
notes = "GECCO-2025 BBSR
TinyGP
A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",
