GECCO 2025 Symbolic Regression Workshop (SymReg)

Part of GECCO 2025
July 14th - 18th, 2025
Malaga, Spain (hybrid event)

Submission Deadline End of March/early April 2025 (to be announced)

Additional Information on GECCO:
https://gecco-2025.sigevo.org

Scope

Symbolic regression is the search for symbolic models that describe a relationship in provided data. Symbolic regression has been one of the first applications of genetic programming and as such is tightly connected to evolutionary algorithms. In recent years several non-evolutionary techniques for solving symbolic regression have emerged, most notably methods based on large language models (LLMs). Especially with the focus on interpretability and explainability in AI research, symbolic regression takes a leading role among machine learning methods, whenever model inspection and understanding is desired.

The focus of this workshop is to further advance the state-of-the-art in symbolic regression and more general equation learning by gathering experts in the field and facilitating an exchange of research ideas. We encourage submissions presenting novel techniques or applications of symbolic regression, theoretical work, or algorithmic improvements to make the techniques more efficient, more reliable, and generally better controlled.

A picture of functions and a generating expression

Particular topics of interest include, but are not limited to:

Evolutionary and non-evolutionary algorithms for symbolic regression
Dealing with uncertainty and model selection
Integration of side-information (physical laws, constraints, ...)
Neuro-symbolic learning
Large-language models for equation learning
Exact and approximate heuristics
Benchmarking symbolic regression algorithms
Symbolic regression applications

Important Dates (to be announced)

Submission via linklings system: https://ssl.linklings.net/conferences/gecco/
Submission deadline: End of March/early April 2025 (to be announced)
Notification of acceptance: End of April
Camera-ready submission: Early May

Previous Workshops

2024 in Melbourne

Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics, H. Liu, H. Lui, Y. Kuang, J. Wang, B. Li
Interactive Symbolic Regression - A Study on Noise Sensitivity and Extrapolation Accuracy, S. Raghav, T. Kumar, R. Balaji, M. Sanjay, C. Shunmuga
Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression, P. Leser, G. Bomarito, G. Kronberger, F. Olivetti de Franca
Accelerating GP Genome Evaluation Through Real Compilation with a Multiple Program Single Data Approach, V.V. de Melo, W. Banzhaf, G. Iacca
Characterising the Double Descent of Symbolic Regression, G. Dick, C. Owen

2023 in Lisbon

Priors for Symbolic Regression, Deaglan Bartlett, Harry Desmond, Pedro Ferreira
Evolving Deformable Mirror Control to Generate Partially Coherent Light Fields, Daniel Younis, Thomas M. Antonsen, Luke A. Johnson, Eric O. Scott, Dimitri Kaganovich, Baham Hafizi
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation, Du Nguyen Duy, Michael Affenzeller, Ramin Nikzad-Langerodi
GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework, Bogdan Burlacu

2022 in Boston

Uncertainty in Equation Learning, Matthias Werner, Andrej Junginger, Philipp Henning, Georg Martius
Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming, David Randall, Tyler Townsend, Jacob Hochhalter, Geoffrey Bomarito
Interaction-Transformation Evolutionary Algorithm with Coefficients Optimization, Guilherme Imai Aldeia, Fabricio de Franca
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression, Marco Virgolin, Peter Bosman
Genetic Programming with Stochastic Gradient Descent Revisited: Initial Findings on SRBench, Grant Dick
Invited Talk From the Winner of the Symbolic Regression Competition