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Produktbild: Genetic Programming Theory and Practice XIX
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Genetic Programming Theory and Practice XIX

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Beschreibung

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...

Beschreibung

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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.03.2024

Abbildungen

XIV, 262 p. 104 illus., 93 illus. in color.

Herausgeber Leonardo Trujillo + weitere

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.03.2024

Abbildungen

XIV, 262 p. 104 illus., 93 illus. in color.

Herausgeber
Verlag Springer Singapore
Seitenzahl

262

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

423 g

Auflage

2023

Sprache

Englisch

ISBN

978-981-19-8462-4

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Genetic Programming Theory and Practice XIX
  • 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.