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Titlebook: Optinformatics in Evolutionary Learning and Optimization; Liang Feng,Yaqing Hou,Zexuan Zhu Book 2021 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 18:40:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Optinformatics in Evolutionary Learning and Optimization
編輯Liang Feng,Yaqing Hou,Zexuan Zhu
視頻videohttp://file.papertrans.cn/704/703371/703371.mp4
概述Summarizes recent algorithmic advances toward realizing the notion of optinformatics in evolutionary learning and optimization.Contains a variety of practical applications, including inter-domain lear
叢書名稱Adaptation, Learning, and Optimization
圖書封面Titlebook: Optinformatics in Evolutionary Learning and Optimization;  Liang Feng,Yaqing Hou,Zexuan Zhu Book 2021 The Editor(s) (if applicable) and The
描述.This book provides readers the recent algorithmic advances towards realizing the notion of .optinformatics. in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of .optinformatics., recent research progresses in this direction, as well as particular algorithm designs and application of .optinformatics...Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch,
出版日期Book 2021
關(guān)鍵詞Optinformatics; Knowledge Reuse; Evolutionary Computation; Memetic Computation; Meta-heuristics; Transfer
版次1
doihttps://doi.org/10.1007/978-3-030-70920-4
isbn_softcover978-3-030-70922-8
isbn_ebook978-3-030-70920-4Series ISSN 1867-4534 Series E-ISSN 1867-4542
issn_series 1867-4534
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:13:47 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:02:26 | 只看該作者
Potential Research Directions,research directions of optinformatics that we believe will be beneficial to the field of evolutionary computation, which are remained to be explored. In this chapter, the possible research directions of optinformatics in evolutionary learning and optimization are discussed.
地板
發(fā)表于 2025-3-22 06:51:01 | 只看該作者
Liang Feng,Yaqing Hou,Zexuan ZhuSummarizes recent algorithmic advances toward realizing the notion of optinformatics in evolutionary learning and optimization.Contains a variety of practical applications, including inter-domain lear
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發(fā)表于 2025-3-22 12:48:10 | 只看該作者
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發(fā)表于 2025-3-22 15:04:19 | 只看該作者
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發(fā)表于 2025-3-22 17:50:23 | 只看該作者
Optinformatics in Evolutionary Learning and Optimization978-3-030-70920-4Series ISSN 1867-4534 Series E-ISSN 1867-4542
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發(fā)表于 2025-3-23 00:14:27 | 只看該作者
Preliminary,s basic units of transferable information encoded in computational representations for enhancing the performance of artificial evolutionary systems in the domain of search and optimization [.], the introduction of memetic computation is also presented in this chapter.
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發(fā)表于 2025-3-23 05:07:27 | 只看該作者
1867-4534 riety of practical applications, including inter-domain lear.This book provides readers the recent algorithmic advances towards realizing the notion of .optinformatics. in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-doma
10#
發(fā)表于 2025-3-23 05:34:20 | 只看該作者
Book 2021. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the reade
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