The authors are all affiliated with the University of Applied Sciences (UAS) Upper Austria.Gabriel Kronberger is professor for data engineering and business intelligence. His research interests are symbolic regression and machine learning as well as probabilistic graphical models.Bogdan Burlacu is a research assistant. His main focus is the study of genetic programming evolutionary dynamics in symbolic regression scenarios.Michael Kommenda is a research assistant. He has been applying symbolic regression methods in various industrial projects and application scenarios.Stephan M. Winkler is professor for medical and bioinformatics and head of the bioinformatics research group. His research interests despite bioinformatics include genetic programming, nonlinear model identification and machine learning.Michael Affenzeller is professor for heuristic optimization and machine learning and head of the Heuristic and Evolutionary Algorithms Laboratory. Furthermore, he is the vice dean for research and overall head of the COMET project for heuristic optimization in production and logistics (HOPL).

• ContentsPrefaceSymbols and Notation1. Introduction2. Basics of Supervised Learning3. Basics of Symbolic Regression4. Evolutionary Computation and Genetic Programming5. Model Validation, Inspection, Simplification and Selection6. Advanced Techniques7. Examples and Applications8. ConclusionAppendixBibliography

The authors are all affiliated with the University of Applied Sciences (UAS) Upper Austria.Gabriel Kronberger is professor for data engineering and business intelligence. His research interests are symbolic regression and machine learning as well as probabilistic graphical models.Bogdan Burlacu is a research assistant. His main focus is the study of genetic programming evolutionary dynamics in symbolic regression scenarios.Michael Kommenda is a research assistant. He has been applying symbolic regression methods in various industrial projects and application scenarios.Stephan M. Winkler is professor for medical and bioinformatics and head of the bioinformatics research group. His research interests despite bioinformatics include genetic programming, nonlinear model identification and machine learning.Michael Affenzeller is professor for heuristic optimization and machine learning and head of the Heuristic and Evolutionary Algorithms Laboratory. Furthermore, he is the vice dean for research and overall head of the COMET project for heuristic optimization in production and logistics (HOPL).

• ContentsPrefaceSymbols and Notation1. Introduction2. Basics of Supervised Learning3. Basics of Symbolic Regression4. Evolutionary Computation and Genetic Programming5. Model Validation, Inspection, Simplification and Selection6. Advanced Techniques7. Examples and Applications8. ConclusionAppendixBibliography

The authors are all affiliated with the University of Applied Sciences (UAS) Upper Austria.Gabriel Kronberger is professor for data engineering and business intelligence. His research interests are symbolic regression and machine learning as well as probabilistic graphical models.Bogdan Burlacu is a research assistant. His main focus is the study of genetic programming evolutionary dynamics in symbolic regression scenarios.Michael Kommenda is a research assistant. He has been applying symbolic regression methods in various industrial projects and application scenarios.Stephan M. Winkler is professor for medical and bioinformatics and head of the bioinformatics research group. His research interests despite bioinformatics include genetic programming, nonlinear model identification and machine learning.Michael Affenzeller is professor for heuristic optimization and machine learning and head of the Heuristic and Evolutionary Algorithms Laboratory. Furthermore, he is the vice dean for research and overall head of the COMET project for heuristic optimization in production and logistics (HOPL).

• ContentsPrefaceSymbols and Notation1. Introduction2. Basics of Supervised Learning3. Basics of Symbolic Regression4. Evolutionary Computation and Genetic Programming5. Model Validation, Inspection, Simplification and Selection6. Advanced Techniques7. Examples and Applications8. ConclusionAppendixBibliography