- author = "Gabriel Kronberger and Bogdan Burlacu and Michael Kommenda and Stephan M. Winkler and Michael Affenzeller",
- title = "Symbolic Regression",
- publisher = "Chapman \& Hall",
- year = "2024",
- edition = "1st",
- month = aug # " 16",
- keywords = "genetic algorithms, genetic programming, LCSH, machine learing, math, model",
- isbn13 = "9781138054813",
-
URL = "
https://www.routledge.com/Symbolic-Regression/Kronberger-Burlacu-Kommenda-Winkler-Affenzeller/p/book/9781138054813",
-
URL = "https://www.amazon.co.uk/Symbolic-Regression-Gabriel-Kronberger/dp/113805481X",
-
DOI = "
doi:10.1201/9781315166407",
- size = "308 pages",
-
abstract = "Symbolic regression (SR) is one of the most powerful
machine learning techniques that produces transparent
models, searching the space of mathematical expressions
for a model that represents the relationship between
the predictors and the dependent variable without the
need of taking assumptions about the model structure.
Currently, the most prevalent learning algorithms for
SR are based on genetic programming (GP), an
evolutionary algorithm inspired from the well-known
principles of natural selection. This book is an
in-depth guide to GP for SR, discussing its advanced
techniques, as well as examples of applications in
science and engineering.
The basic idea of GP is to evolve a population of solution candidates in an iterative, generational manner, by repeated application of selection, crossover, mutation, and replacement, thus allowing the model structure, coefficients, and input variables to be searched simultaneously. Given that explainability and interpretability are key elements for integrating humans into the loop of learning in AI, increasing the capacity for data scientists to understand internal algorithmic processes and their resultant models has beneficial implications for the learning process as a whole.
This book represents a practical guide for industry professionals and students across a range of disciplines, particularly data science, engineering, and applied mathematics. Focused on state-of-the-art SR methods and providing ready-to-use recipes, this book is especially appealing to those working with empirical or semi-analytical models in science and engineering.
Table of Contents
Contents
Preface
Symbols and Notation
1. Introduction 2. Basics of Supervised Learning 3. Basics of Symbolic Regression 4. Evolutionary Computation and Genetic Programming 5. Model Validation, Inspection, Simplification and Selection 6. Advanced Techniques 7. Examples and Applications 8. Conclusion Appendix Bibliography",
-
notes = "HeuristicLab, Operon, PySR, DEAP, FEAT, ECJ, GPTIPS.
DataModeler, Eureqa Formulize
University of Applied Sciences (UAS) Upper Austria",
