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.

• 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.

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.

• 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.

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.

• 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.