Handbook of Evolutionary Machine Learning: (Genetic and Evolutionary Computation)

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.

Table of Contents:
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.

About the Author :
Wolfgang Banzhaf is a professor in the Department of Computer Science and Engineering at Michigan State University. He is the John R. Koza Endowed Chair in Genetic Programming and a member of the BEACON Center for the Study of Evolution in Action. His research interests include evolutionary computation and complex adaptive systems. Studies of self-organization and the field of Artificial Life are also of very much interest to him. Penousal Machado is an associate professor in the Department of Informatics at the University of Coimbra in Portugal, the coordinator of the Cognitive and Media Systems group of the Centre for Informatics and Systems of the University of Coimbra (CISUC), and the scientific director of the Computational Design and Visualization Lab of CISUC. His research interests include evolutionary computation, computational creativity, artificial intelligence, and information visualization. Mengjie Zhang is a Professor of Computer Science, Head of the Evolutionary Computation and machine learning Research Group, and Director of Data Science and Artificial Intelligence, Victoria University of Wellington, New Zealand. His current research interests include artificial intelligence and machine learning, particularly genetic programming, image analysis, feature selection and reduction, job shop scheduling, and transfer learning.