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Titlebook: Evolutionary Computation in Combinatorial Optimization; 22nd European Confer Leslie Pérez Cáceres,Sébastien Verel Conference proceedings 20

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發(fā)表于 2025-3-21 17:52:28 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Evolutionary Computation in Combinatorial Optimization
副標(biāo)題22nd European Confer
編輯Leslie Pérez Cáceres,Sébastien Verel
視頻videohttp://file.papertrans.cn/318/317889/317889.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Evolutionary Computation in Combinatorial Optimization; 22nd European Confer Leslie Pérez Cáceres,Sébastien Verel Conference proceedings 20
描述This book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP..The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields..
出版日期Conference proceedings 2022
關(guān)鍵詞ant colony optimization; artificial intelligence; automatic algorithms configuration; beam search; combi
版次1
doihttps://doi.org/10.1007/978-3-031-04148-8
isbn_softcover978-3-031-04147-1
isbn_ebook978-3-031-04148-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
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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發(fā)表于 2025-3-21 20:25:12 | 只看該作者
Conference proceedings 2022e 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields..
板凳
發(fā)表于 2025-3-22 03:06:47 | 只看該作者
0302-9743 EuroGP..The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields..978-3-031-04147-1978-3-031-04148-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
地板
發(fā)表于 2025-3-22 07:36:41 | 只看該作者
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Wim Dubbink,Luc Liedekerke,Henk Luijk various simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess the value of each studied combination. We also provide empirical evidence to shed light on the advantages and limits of each strategy.
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發(fā)表于 2025-3-22 14:45:03 | 只看該作者
https://doi.org/10.1057/9780230287068ve been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human-based heuristics and classical heuristic generation approaches.
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發(fā)表于 2025-3-22 17:45:17 | 只看該作者
Toward a Global European Cinemaizing multiple levels of roles simultaneously. The other approaches divide the multi-level role mining problem into separate sub-problems, which are optimized individually. All approaches are based on an evolutionary algorithm for single-level role mining and have been implemented and evaluated in a range of experiments.
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發(fā)表于 2025-3-23 00:31:33 | 只看該作者
European Cinema and Intertextuality. The results show that the proposed approach can obtain better and simpler rules than the baseline GP and existing GP algorithms with simplification. Further analysis verified the effectiveness of the newly developed numerical and simplification operators.
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發(fā)表于 2025-3-23 04:26:14 | 只看該作者
Sophie De Vinck,Caroline Pauwelsem. We consider truss optimisation problem instances where designers need to select the size of bars from a discrete set with respect to practice code constraints. Our experimental investigations show that our approach outperforms the current state-of-the-art methods and it obtains multiple high-quality solutions.
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發(fā)表于 2025-3-23 08:52:36 | 只看該作者
https://doi.org/10.1057/9780230295070ty issue of the guidance heuristic, a cut-off approach is presented that reduces large instances to smaller ones. The proposed approaches are tested on two established sets of benchmark instances. MPBS guided by AEL outperforms the so far leading method on average on a set of real instances. For many instances new best solutions could be obtained.
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