Genetic Programming Theory and Practice XIX

2023
ISBN: 978-981-19-8459-4
Verlag: Springer Nature Singapore

Buch, Englisch, 262 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 582 g

Reihe: Genetic and Evolutionary Computation

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 year´s 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.

Zielgruppe

Research

Autoren/Hrsg.

Trujillo, Leonardo

Banzhaf, Wolfgang

Winkler, Stephan M.

Silva, Sara

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

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 InferentialKnowledge.- 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.