Handbook of Evolutionary Machine Learning [electronic resource] /

Part 1. Overview chapters -- Chapter 1. EML Fundamentals -- Chapter 2. EML in Supervised Learning -- Chapter 3. EML in Unsupervised Learning -- Chapter 4. EML in Reinforcement Learning -- Part 2. Evolutionary Computation as Machine Learning -- Chapter 5. Evolutionary Clustering -- Chapter 6. Evolutionary Classification and Regression -- Chapter 7. Evolutionary Ensemble Learning -- Chapter 8. Evolutionary Deep Learning -- Chapter 9. Evolutionary Generative Models -- Part 3. Evolutionary Computation for Machine Learning -- Chapter 10. Evolutionary Data Preparation -- Chapter 11. Evolutionary Feature Engineering and Selection -- Chapter 12. Evolutionary Model Parametrization -- Chapter 13. Evolutionary Model Design -- Chapter 14. Evolutionary Model Validation -- Part 4. Applications -- Chapter 15. EML in Medicine -- Chapter 16. EML in Robotics -- Chapter 17. EML in Finance -- Chapter 18. EML in Science -- Chapter 19. EML in Environmental Science -- Chapter 20. EML in the Arts.

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.