Genetic programming theory and practice XIX / Leonardo Trujillo, Stephan M. Winkler, Sara Silva, Wolfgang Banzhaf, editors.
Trujillo, Leonardo, editor.; Winkler, Stephan M., editor.; Silva, Sara, editor.; Banzhaf, Wolfgang, 1955- editor.
2023
QA76.623
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Title Genetic programming theory and practice XIX / Leonardo Trujillo, Stephan M. Winkler, Sara Silva, Wolfgang Banzhaf, editors.
ISBN 9789811984600 (electronic bk.)
9811984603 (electronic bk.)
981198459X
9789811984594
DOI https://doi.org/10.1007/978-981-19-8460-0
Published Singapore : Springer, [2023]
Copyright ©2023
Language English
Description 1 online resource (xiv, 262 pages) : illustrations (chiefly color).
Item Number 10.1007/978-981-19-8460-0 doi
Call Number QA76.623
Dewey Decimal Classification 006.3/1
Summary This book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this years book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the art in GP research.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Trujillo, Leonardo, editor.
Winkler, Stephan M., editor.
Silva, Sara, editor.
Banzhaf, Wolfgang, 1955- editor.
Series Genetic and evolutionary computation series.
Available in Other Form GENETIC PROGRAMMING THEORY AND PRACTICE XIX.
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Chapter 1. Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
Chapter 2. Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
Chapter 3. GUI-Based, Efficient Genetic Programming and AI Planning For Unity3D
Chapter 4. Genetic Programming for Interpretable and Explainable Machine Learning
Chapter 5. Biological Strategies ParetoGP Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems
Chapter 6. GP-Based Generative Adversarial Models
Chapter 7. Modelling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification through Inferential Knowledge
Chapter 8. Life as a Cyber-Bio-Physical System
Chapter 9. STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison
Chapter 10. Evolving Complexity is Hard
Chapter 11. ESSAY: Computers Are Useless ... They Only Give Us Answers.

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