標題: Titlebook: Genetic Programming for Production Scheduling; An Evolutionary Lear Fangfang Zhang,Su Nguyen,Mengjie Zhang Book 2021 The Editor(s) (if appl [打印本頁] 作者: 恰當 時間: 2025-3-21 19:41
書目名稱Genetic Programming for Production Scheduling影響因子(影響力)
書目名稱Genetic Programming for Production Scheduling影響因子(影響力)學科排名
書目名稱Genetic Programming for Production Scheduling網(wǎng)絡公開度
書目名稱Genetic Programming for Production Scheduling網(wǎng)絡公開度學科排名
書目名稱Genetic Programming for Production Scheduling被引頻次
書目名稱Genetic Programming for Production Scheduling被引頻次學科排名
書目名稱Genetic Programming for Production Scheduling年度引用
書目名稱Genetic Programming for Production Scheduling年度引用學科排名
書目名稱Genetic Programming for Production Scheduling讀者反饋
書目名稱Genetic Programming for Production Scheduling讀者反饋學科排名
作者: 競選運動 時間: 2025-3-21 20:45
Machine Learning: Foundations, Methodologies, and Applications382618.jpg作者: 四目在模仿 時間: 2025-3-22 01:10 作者: ANTI 時間: 2025-3-22 08:22
,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.作者: endure 時間: 2025-3-22 08:54 作者: 匍匐 時間: 2025-3-22 16:04 作者: 匍匐 時間: 2025-3-22 20:35
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作者: 詞匯記憶方法 時間: 2025-3-23 01:11 作者: Eviction 時間: 2025-3-23 04:09
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作者: angiography 時間: 2025-3-23 07:24
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作者: 航海太平洋 時間: 2025-3-23 10:37
Partnerschap in het UMC St Radboud,exible job shop scheduling. Two strategies are introduced, one is the genetic programming with cooperative coevolution, the other is the genetic programming with multi-tree representation. The results show the advantages and disadvantages of these two strategies over learning two rules simultaneousl作者: anarchist 時間: 2025-3-23 17:04
https://doi.org/10.1007/978-90-368-2580-1uling. How to design multiple surrogate models and how to share knowledge among the built surrogates are introduced. The results show that the proposed algorithm can significantly reduce the training time to learn scheduling heuristics for dynamic scheduling. With the same training time, the propose作者: compose 時間: 2025-3-23 20:31 作者: 懶鬼才會衰弱 時間: 2025-3-23 23:22 作者: Accede 時間: 2025-3-24 02:26 作者: 遵循的規(guī)范 時間: 2025-3-24 10:05 作者: Lipoprotein(A) 時間: 2025-3-24 14:16 作者: 碎石頭 時間: 2025-3-24 18:04 作者: 背書 時間: 2025-3-24 19:15
,Loek Winter: ‘Ik maak van zand cement’,ow to measure the relatedness between dynamic scheduling tasks, and how to use the relatedness information to choose assisted task to enhance positive knowledge transfer between tasks. The results show that the proposed task relatedness measure can detect related tasks effectively and sharing knowle作者: 誹謗 時間: 2025-3-25 02:28
https://doi.org/10.1007/978-981-16-4859-5Production Scheduling; Machine Learning; Hyper-Heuristic Learning; Genetic Programming; Multitask Optimi作者: Stagger 時間: 2025-3-25 03:26
978-981-16-4861-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Notorious 時間: 2025-3-25 10:25
Genetic Programming for Production Scheduling978-981-16-4859-5Series ISSN 2730-9908 Series E-ISSN 2730-9916 作者: aphasia 時間: 2025-3-25 12:24
Fangfang Zhang,Su Nguyen,Mengjie ZhangPresents 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作者: 機械 時間: 2025-3-25 19:45 作者: Salivary-Gland 時間: 2025-3-25 22:56 作者: 含糊其辭 時間: 2025-3-26 01:46
Learning Schedule Construction Heuristicseduling algorithms. Details about attributes extracted from production data and representations of scheduling construction heuristics are provided in this chapter. The advantages and disadvantages of each representation are analysed, and the generalisation of evolved heuristics is examined by using 作者: CLAMP 時間: 2025-3-26 07:09
Learning Schedule Improvement Heuristicss 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作者: 骨 時間: 2025-3-26 12:04
Learning to Augment Operations Research Algorithmsing. 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作者: Blazon 時間: 2025-3-26 14:50
Representations with Multi-tree and Cooperative Coevolutionexible job shop scheduling. Two strategies are introduced, one is the genetic programming with cooperative coevolution, the other is the genetic programming with multi-tree representation. The results show the advantages and disadvantages of these two strategies over learning two rules simultaneousl作者: parasite 時間: 2025-3-26 17:00 作者: 虛構(gòu)的東西 時間: 2025-3-26 21:04 作者: 強行引入 時間: 2025-3-27 04:37 作者: Needlework 時間: 2025-3-27 06:46 作者: 假 時間: 2025-3-27 10:40 作者: 輕浮思想 時間: 2025-3-27 16:48 作者: uncertain 時間: 2025-3-27 18:04
Multitask Learning in Hyper-Heuristic Domain with Dynamic Production Schedulingistic domain with genetic programming for this purpose. This chapter verifies the effectiveness of traditional multitask learning in genetic programming for dynamic scheduling and identifies a number of differences that need to be adapted. This chapter develops a multi-population multitask learning 作者: 放縱 時間: 2025-3-27 23:49 作者: LARK 時間: 2025-3-28 02:31 作者: Aspiration 時間: 2025-3-28 07:14 作者: 絕食 時間: 2025-3-28 10:38
https://doi.org/10.1007/978-90-368-2580-1d algorithm can learn better scheduling heuristics than the state-of-the-art surrogate-assisted genetic programming algorithms. Further analyses of the knowledge transfer strategy including the transfer ratio are also studied in this chapter.作者: inculpate 時間: 2025-3-28 16:50 作者: 陪審團每個人 時間: 2025-3-28 20:03
De sociale ontwikkeling van het schoolkinduristics designed manually in the literature. Further analyses of the obtained Pareto fronts also show that the evolved heuristics are robust and the multi-objective algorithms can discover many interesting heuristics which have never been explored by researchers in past studies.作者: MUT 時間: 2025-3-29 00:29 作者: 任命 時間: 2025-3-29 03:22 作者: 糾纏 時間: 2025-3-29 10:56 作者: prolate 時間: 2025-3-29 12:31
,Loek Winter: ‘Ik maak van zand cement’,dge between related tasks can help learn effective scheduling heuristics for a task. In addition, the relatedness between tasks and the selected assisted tasks are also analysed. Last, the factors that contribute to the effectiveness improvement are studied.作者: 有害處 時間: 2025-3-29 15:51 作者: 淡紫色花 時間: 2025-3-29 23:30 作者: Inscrutable 時間: 2025-3-30 03:32 作者: grounded 時間: 2025-3-30 05:18
Search Space Reduction with Feature Selection and have a small number of nodes and thus tend to be more interpretable. The important features for the routing rule and the sequencing rule are further analysed, and the results show that features have different importance for these two rules. In addition, the importance of features differs in different scenarios.作者: KIN 時間: 2025-3-30 11:40
Learning Heuristics for Multi-objective Dynamic Production Scheduling Problemsuristics designed manually in the literature. Further analyses of the obtained Pareto fronts also show that the evolved heuristics are robust and the multi-objective algorithms can discover many interesting heuristics which have never been explored by researchers in past studies.作者: 自戀 時間: 2025-3-30 14:16
Cooperative Coevolution for Multi-objective Production Scheduling Problems that evolved scheduling heuristics outperforms different sophisticated heuristics proposed in the literature. The complexity of the cooperative coevolution technique and the generalisation of evolved scheduling heuristics are further analysed to highlight the effectiveness of the proposed algorithm.
