GECCO 2025 | Symbolic Regression Workshop

Symbolic Regression Workshop

Description

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 by a domain expert 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.

Organizers

Gabriel Kronberger

Gabriel Kronberger is professor at the University of Applied Sciences Upper Austria and has been working on algorithms for symbolic regression since more than 15 years. From 2018 until 2022 he led the Josef Ressel Center for Symbolic Regression. In 2024, he published a book on "Symbolic Regression" together with Burlacu, Kommenda, Winkler, and Affenzeller. His current research interests are symbolic regression for physics-based machine learning and applications in science and engineering. Gabriel has (co-)authored more than 100 publications (SCOPUS) and has been a member of the Program Committee for the GECCO Genetic Programming track since 2016. More information: (https://symreg.at)

Fabricio Olivetti de França

Fabricio is an Associated Professor at Federal University of ABC (UFABC), Brazil.
He received his MsC and PhD from State University of Campinas (UNICAMP) with a focus on
data clustering and multimodal optimization. His current research focuses on interpretable
models with Symbolic Regression and real-world applications.

William La Cava

William La Cava is an Assistant Professor in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital and Harvard Medical School. He received his PhD from UMass Amherst with a focus on interpretable modeling of dynamical systems. Prior to joining CHIP, he was a post-doctoral fellow and research associate in the Institute for Biomedical Informatics at the University of Pennsylvania.

Steven Gustafson

Steven Gustafson received his PhD in Computer Science and Artificial Intelligence, and shortly thereafter was awarded IEEE Intelligent System's "AI's 10 to Watch" for his work in algorithms that discover algorithms. For 10+ years at GE's corporate R&D center he was a leader
in AI, successful technical lab manager, all while inventing and deploying state-of-the-art AI systems for almost every GE business, from GE Capital to NBC Universal and GE Aviation. He has over 50 publications, 13 patents, was a co-founder and Technical Editor in Chief of the Memetic Computing Journal. Steven has chaired various conferences and workshops, including the first Symbolic Regression and Modeling (SRM) Workshop at GECCO2009 and subsequent workshops from 2010 to 2014. As the Chief Scientist at Maana, a Knowledge Platform software company, he invented and architected new AutoML and NLP techniques with publications in AAAI and IJCAI. Dr. Gustafson was the
CTO at Noonum, a FinTech startup that delivers insights on companies and markets using advances in NLP and AI and the Chief Scientist at BigFilter.ai a company focused on AI safety and alignment technology. Currently he is assistant professor at the University of
Washington.