| 書目名稱 | Genetic Algorithm Essentials | | 編輯 | Oliver Kramer | | 視頻video | http://file.papertrans.cn/383/382432/382432.mp4 | | 概述 | Provides an essential introduction to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible.Presents an overview of | | 叢書名稱 | Studies in Computational Intelligence | | 圖書封面 |  | | 描述 | This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations..The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.. | | 出版日期 | Book 2017 | | 關(guān)鍵詞 | Introduction to GA; Evolutionary Operators; Solution Space Variants; Computational Intelligence; Intelli | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-52156-5 | | isbn_softcover | 978-3-319-84834-1 | | isbn_ebook | 978-3-319-52156-5Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer International Publishing AG, part of Springer Nature 2017 |
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