Gabriel Kronberger

Gabriel Kronberger is a Professor at Hagenberg University of Applied Sciences, specializing in Symbolic Regression, Genetic Programming, and Machine Learning. His work bridges theoretical advancements with industrial applications in mechatronics and engineering systems.

• Active in evolutionary computation and symbolic regression since 2006
• Lead researcher at the Josef Ressel Center for Symbolic Regression
• Developed techniques for alarm flood reduction in critical infrastructure

His research focuses on Symbolic Regression, where he explores algorithmic enhancements like redundant parameter reduction and equality graph integration. He applies these methods to material science (e.g., tensile strength prediction) and automotive engineering (e.g., powertrain modeling).

Recent publications demonstrate a trend toward interactive tools (rEGGression) and hybrid approaches combining genetic programming with machine learning systems (neural networks, random forests). All 158 publications emphasize practical implementations in industrial contexts.

He has organized key conferences like Genetic and Evolutionary Computation Conference (2017-2020) and led 6 major research projects from 2013 to 2026, including EREMA Recycling 4.0 and McTronic educational initiatives.