GECCO 2024 Symbolic Regression Workshop (SymReg)

Part of GECCO 2024
July 14th - 18th, 2024
Melbourne, Australia (hybrid event)

Submission Deadline (extended): April 12th, 2024

Additional Information on GECCO:
https://gecco-2024.sigevo.org
https://gecco-2024.sigevo.org/Workshops#SymReg

Scope

Symbolic regression designates 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. 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 by a domain expert is desired. Examples where symbolic regression already produces outstanding results include modeling where interpretability is desired, modeling of non-linear dependencies, modeling with small data sets or noisy data, modeling with additional constraints, or modeling of differential equation systems.

The focus of this workshop is to further advance the state-of-the-art in symbolic regression by gathering experts in the field of symbolic regression and facilitating an exchange of novel research ideas. Therefore, we encourage submissions presenting novel techniques or applications of symbolic regression, theoretical work on issues of generalization, size and interpretability of the models produced, or algorithmic improvements to make the techniques 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
Improving stability of symbolic regression algorithms
Model selection
Uncertainty quantification
Integration of side-information (physical laws, constraints, ...)
Benchmarking symbolic regression algorithms
Symbolic regression for scientific machine learning
Innovative symbolic regression applications

Important Dates

Submission opening: February 12th, 2024
Submission deadline: April 12th, 2024 (extended)
Notification of acceptance: May 3rd, 2024
Camera-ready submission: May 10th, 2024 (!)

Previous Workshops

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