Genetic Programming Theory and Practice XXI

Inbunden, Engelska, 2025

AvStephan M. Winkler,Wolfgang Banzhaf,Ting Hu,Alexander Lalejini

2 879 kr

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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.

Produktinformation

Utgivningsdatum2025-03-01
Mått155 x 235 x 28 mm
Vikt887 g
FormatInbunden
SpråkEngelska
SerieGenetic and Evolutionary Computation
Antal sidor417
FörlagSpringer Nature Switzerland AG
ISBN9789819600762

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Artificiell intelligens inom Data och it

Chapter 1. Representation & Reachability: Assumption Impact in Data Modeling.- Chapter 2. EvoFeat: Genetic Programming-based Feature Engineering Approach to Tabular Data Classification.- Chapter 3. Deep Learning-Based Operators for Evolutionary Algorithms.- Chapter 4. Survey of Genetic Programming and Large Language Models.- Chapter 5. Evolving Many-Model Agents with Vector and Matrix Operations in Tangled Program Graphs.- Chapter 6. Automatic Design of Autoencoders using NeuroEvolution.- Chapter 7. Code Building Genetic Programming is Faster than PushGP.- Chapter 8. Sharpness-Aware Minimization in Genetic Programming.- Chapter 9. Tree-Based Grammatical Evolution with Non-Encoding Nodes.- Chapter 10. Genetic Programming with Memory for Approximate Data Reconstruction.- Chapter 11. Ratcheted Random Search for Self-Programming Boolean Networks.- Chapter 12. Exploring Non-Bloating Geometric Semantic Genetic Programming.- Chapter 13. Revisiting Gradient-based Local Search in Symbolic Regression.- Chapter 14. It’s Time to Revisit the Use of FPGAs for Genetic Programming.- Chapter 15. Interpretable Genetic Programming Models for Real-WorldBiomedical Images.- Chapter 16. Crafting Generative Art through Genetic Improvement: Managing Creative Outputs in Diverse Fitness Landscapes.- Chapter 17. Cell Regulation and the Early Evolution of Autonomous Control.- Chapter 18. How to Measure Explainability and Interpretability of Machine Learning Results.- Chapter 19. Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics.- Chapter 20. Using lineage age to augment search space exploration in lexicase selection.