- author = "Christian Haider and Fabricio Olivetti {de Franca} and Bogdan Burlacu and Florian Bachinger and Gabriel Kronberger and Michael Affenzeller",
- title = "Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation",
- booktitle = "Genetic Programming Theory and Practice XX",
- year = "2023",
- editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu",
- series = "Genetic and Evolutionary Computation",
- pages = "225--240",
- address = "Michigan State University, USA",
- month = jun # " 1-3",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-981-99-8412-1",
-
DOI = "
doi:10.1007/978-981-99-8413-8_12",
- abstract = "We present different approaches for including knowledge in data-based modeling. For this, we use the model representation of symbolic regression (SR), which represents the models as short interpretable mathematical formulas. The integration of knowledge into symbolic regression via shape constraints is discussed alongside three real-world applications: modeling magnetisation curves, modeling twin-screw extruders and model-based data validation.",
-
notes = "
Part of \cite{Hu:2023:GPTP} published after the workshop in 2024",
