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Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

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Genetic Programming Theory and Practice XX

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

We present different approaches for including knowledge in data-based modeling. For this, we utilize 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 magnetization curves, modeling twin-screw extruders and model-based data validation.

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Acknowledgements

The authors gratefully acknowledge the federal state of Upper Austria for funding the research project FinCoM (Financial Condition Monitoring) and thus, the underlying research of this study. Furthermore, the authors thank the federal state of Upper Austria as part of the program “#upperVISION2030” for funding the research project SPA (Secure Prescriptive Analytics) and thus, the research of parts of this study. This project was partially funded by Fundaçao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), grant number 2021/12706-1.

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Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory (HEAL), University of Applied Sciences Upper Austria, Hagenberg, Austria

    Christian Haider, Bogdan Burlacu, Florian Bachinger, Gabriel Kronberger & Michael Affenzeller

  2. Center for Mathematics, Computation and Cognition (CMCC), Heuristics, Analysis and Learning Laboratory (HAL), Federal University of ABC, Santo Andre, Brazil

    Fabricio Olivetti de Franca

Authors
  1. Christian Haider
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  2. Fabricio Olivetti de Franca
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  3. Bogdan Burlacu
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  4. Florian Bachinger
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  5. Gabriel Kronberger
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  6. Michael Affenzeller
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Corresponding author

Correspondence to Christian Haider .

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Editors and Affiliations

  1. School of Information, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  2. Engineering Sciences Graduate Program, Tecnológico Nacional de México, IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Charles Ofria

  4. School of Computing, Queen's University, Kingston, ON, Canada

    Ting Hu

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Haider, C., de Franca, F.O., Burlacu, B., Bachinger, F., Kronberger, G., Affenzeller, M. (2024). Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_12

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  • DOI: https://doi.org/10.1007/978-981-99-8413-8_12

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  • Online ISBN: 978-981-99-8413-8

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