书目名称 | Handbook of Evolutionary Machine Learning | 编辑 | Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang | 视频video | | 概述 | 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 | 丛书名称 | Genetic and Evolutionary Computation | 图书封面 |  | 描述 | 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, finan | 出版日期 | Book 2024 | 关键词 | Machine Learning; Artificial Evolution; Data Analysis; Evolutionary Deep Learning; Evolutionary Feature | 版次 | 1 | doi | https://doi.org/10.1007/978-981-99-3814-8 | isbn_softcover | 978-981-99-3816-2 | isbn_ebook | 978-981-99-3814-8Series ISSN 1932-0167 Series E-ISSN 1932-0175 | issn_series | 1932-0167 | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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