Overview
- Explores various ways evolution can help improve current methods of machine learning
- Presents real-world applications in medicine, robotics, science, finance, and other domains
- Serves as an essential reference for those interested in evolutionary approaches to machine learning
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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About this book
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.
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Keywords
Table of contents (26 chapters)
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Evolutionary Machine Learning Basics
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Evolutionary Computation as Machine Learning
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Evolutionary Computation for Machine Learning
Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Handbook of Evolutionary Machine Learning
Editors: Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-981-99-3814-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-99-3813-1Published: 02 November 2023
Softcover ISBN: 978-981-99-3816-2Due: 15 November 2024
eBook ISBN: 978-981-99-3814-8Published: 01 November 2023
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XVI, 768
Number of Illustrations: 54 b/w illustrations, 148 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Computational Intelligence, Evolutionary Biology