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Titlebook: Genetic Programming for Production Scheduling; An Evolutionary Lear Fangfang Zhang,Su Nguyen,Mengjie Zhang Book 2021 The Editor(s) (if appl

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發(fā)表于 2025-3-21 19:41:06 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Genetic Programming for Production Scheduling
副標題An Evolutionary Lear
編輯Fangfang Zhang,Su Nguyen,Mengjie Zhang
視頻videohttp://file.papertrans.cn/383/382618/382618.mp4
概述Presents theoretical aspects and applications of genetic programming for production scheduling.Explores the modern and unique interfaces between operations research and machine learning.Offers an intr
叢書名稱Machine Learning: Foundations, Methodologies, and Applications
圖書封面Titlebook: Genetic Programming for Production Scheduling; An Evolutionary Lear Fangfang Zhang,Su Nguyen,Mengjie Zhang Book 2021 The Editor(s) (if appl
描述.This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP’s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning te
出版日期Book 2021
關鍵詞Production Scheduling; Machine Learning; Hyper-Heuristic Learning; Genetic Programming; Multitask Optimi
版次1
doihttps://doi.org/10.1007/978-981-16-4859-5
isbn_softcover978-981-16-4861-8
isbn_ebook978-981-16-4859-5Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
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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沙發(fā)
發(fā)表于 2025-3-21 20:45:35 | 只看該作者
Machine Learning: Foundations, Methodologies, and Applications382618.jpg
板凳
發(fā)表于 2025-3-22 01:10:50 | 只看該作者
地板
發(fā)表于 2025-3-22 08:22:05 | 只看該作者
,Met diabetes ‘moet’ je gewoon leven,roaches, especially genetic programming as well as the overview to use genetic programming for production scheduling. In addition, this chapter introduces interpretable machine learning. Last, the terminology and organisation of the book are introduced to make it easy for readers to follow this book.
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發(fā)表于 2025-3-22 08:54:19 | 只看該作者
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發(fā)表于 2025-3-22 20:35:02 | 只看該作者
De ontwikkeling van de grove motoriek,exact methods, heuristics, and hyper-heuristics, with a focus on hyper-heuristics in evolutionary learning. This chapter also describes how to use scheduling heuristics to handle job shop scheduling problems. In addition, how to use genetic programming to learn scheduling heuristics is introduced in
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發(fā)表于 2025-3-23 04:09:48 | 只看該作者
https://doi.org/10.1007/978-90-313-6299-8s presented in this book and other meta-heuristics in the literature. Extended attribute sets and several evaluation mechanisms are introduced in this chapter to allow GP to evolve scheduling improvement heuristics. Experiment results show that the evolved scheduling improvement heuristics outperfor
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發(fā)表于 2025-3-23 07:24:21 | 只看該作者
https://doi.org/10.1007/978-90-368-0727-2ing. A simple genetic programming algorithm is introduced to evolve variable selectors for optimisation solvers to reduce the computational efforts required to obtain high-quality or optimal solutions for production scheduling. The optimisation solver enhanced by the evolved variable selectors can f
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