Workshop on Symbolic Regression and Equation Discovery

Part of the 2026 IEEE World Congress on Computational Intelligence (WCCI) and the IEEE Congress on Evolutionary Computation (CEC)
June 21st - 26th, 2026
Maastricht, Netherlands

We call for submissions of research papers of maximally 6 pages. Submissions must adhere to the WCCI/CEC submission rules and guidelines to authors. Accepted papers must presented by at least one author at the required time slots of approximately 20 minutes at the conference. Presenters have to register for the conference.

Submission Deadline April 10th, 2026 23:59 (anywhere on Earth) via openreview

Additional Information on WCCI / CEC:
https://attend.ieee.org/wcci-2026/
https://attend.ieee.org/wcci-2026/ieee-cec-2025/

A picture of functions and a generating expression

Scope

Symbolic regression (SR) is a form of automated equation discovery from data using computational intelligence techniques. SR was first introduced by John Koza as a specific task that can be approached using genetic programming, and as such is tightly connected to evolutionary algorithms. However, equation discovery from data has been a goal of artificial intelligence (AI) research since its early beginnings. In recent years, several novel techniques have emerged, most notably methods based on deep (reinforcement) learning and large language models (LLMs). Especially with the focus on interpretability and explainability in AI research, SR 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 SR and more general equation discovery, by gathering experts in the field, and facilitating an exchange of research ideas. We encourage submissions presenting novel techniques or applications of SR, theoretical work, or algorithmic improvements to make SR methods more efficient, more reliable, and generally better controlled.

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

Evolutionary and non-evolutionary algorithms for symbolic regression
Dealing with uncertainty and model selection
Knowledge integration (physical laws, constraints, ...)
Neuro-symbolic learning and large language models for equation learning
Benchmarking symbolic regression algorithms
Symbolic regression applications

Important Dates

Submission deadline: April 10th, 2026 via openreview.net (to be announced)
Notification of acceptance: May 8th, 2026

Previous Workshops

2025 in Malaga (GECCO)

SCRBenchmark: A Benchmarking Library for Shape-Constrained Regression, Bachinger, Werth, Zenisek, Haider, Olivetti de Franca
Continuous Pruning for Symbolic Regression, Werth, Affenzeller
When Data Transformations Mislead Symbolic Regression: Deceptive Search Spaces in System Identification, Tonda, Zhang, Chen, Xue, Zhang, Lutton
Call for Action: towards the next generation of symbolic regression benchmark, Imai Aldeia, Zhang, Bomarito, Cranmer, Fonseca, Burlacu, La Cava, de França
Rewarding Model Smoothness and Simplicity via Alternating Objectives in Symbolic Regression, Haut, Kotanchek
Model Recovery in Symbolic Regression: Theory, Conjectures, and Open Questions, Senn
A Hierarchical Multiview Symbolic Regression Method for Decoding Oceanic Metabolism, Lira, Martí, Sanchez-Pi
On the use of Hinge Loss as a Surrogate Fitness Function with Grammatical Evolution for Parkinson’s Disease Classification, Duan, Nicolau, O'Neill
Can Synthetic Data Improve Symbolic Regression Extrapolation Performance?, Ramlan, O'Riordan, Kronberger, McDermott

2024 in Melbourne (GECCO)

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 (GECCO)

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 (GECCO)

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