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Titlebook: Handbook of Evolutionary Machine Learning; Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang Book 2024 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 19:33:48 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Handbook of Evolutionary Machine Learning
編輯Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang
視頻videohttp://file.papertrans.cn/422/421300/421300.mp4
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
叢書名稱Genetic and Evolutionary Computation
圖書封面Titlebook: Handbook of Evolutionary Machine Learning;  Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang Book 2024 The Editor(s) (if applicable) and The
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
關(guān)鍵詞Machine Learning; Artificial Evolution; Data Analysis; Evolutionary Deep Learning; Evolutionary Feature
版次1
doihttps://doi.org/10.1007/978-981-99-3814-8
isbn_softcover978-981-99-3816-2
isbn_ebook978-981-99-3814-8Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 21:20:20 | 只看該作者
Wolfgang Banzhaf,Penousal Machado,Mengjie ZhangExplores 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
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發(fā)表于 2025-3-22 03:48:16 | 只看該作者
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發(fā)表于 2025-3-22 06:51:37 | 只看該作者
978-981-99-3816-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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發(fā)表于 2025-3-22 11:25:49 | 只看該作者
Handbook of Evolutionary Machine Learning978-981-99-3814-8Series ISSN 1932-0167 Series E-ISSN 1932-0175
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發(fā)表于 2025-3-22 16:26:42 | 只看該作者
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發(fā)表于 2025-3-22 19:32:51 | 只看該作者
Fundamentals of Evolutionary Machine Learningrstand evolutionary machine learning. Then we take a look at its roots, finding that it has quite a long history, going back to the 1950s. We introduce a taxonomy of the field, discuss the major branches of evolutionary machine learning, and conclude by outlining open problems.
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發(fā)表于 2025-3-22 22:35:06 | 只看該作者
Eberhard Schaich,Alfred HamerleThis chapter provides an overview of evolutionary approaches to supervised learning. It starts with the definition and scope of the opportunity, and then reviews three main areas: evolving general neural network designs, evolving solutions that are explainable, and forming a synergy of evolutionary and gradient-based methods.
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1932-0167 science, finance, and other domains.Serves as an essential 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 bo
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