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Titlebook: Genetic Programming; 16th European Confe Krzysztof Krawiec,Alberto Moraglio,Bin Hu Conference proceedings 2013 Springer-Verlag Berlin Heid

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發(fā)表于 2025-3-21 19:54:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Genetic Programming
副標(biāo)題16th European Confe
編輯Krzysztof Krawiec,Alberto Moraglio,Bin Hu
視頻videohttp://file.papertrans.cn/383/382569/382569.mp4
概述Fast track conference proceedings.Unique visibility
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Genetic Programming; 16th European  Confe Krzysztof Krawiec,Alberto Moraglio,Bin Hu Conference proceedings 2013 Springer-Verlag Berlin Heid
描述This book constitutes the refereed proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, held in Vienna, Austria, in April 2013 co-located with the Evo* 2013 events, EvoMUSART, EvoCOP, EvoBIO, and EvoApplications. .The 18 revised full papers presented together with 5 poster papers were carefully reviewed and selected from 47 submissions. The wide range of topics in this volume reflects the current state of research in the field, including different genres of GP (tree-based, linear, grammar-based, Cartesian), theory, novel operators, and applications.
出版日期Conference proceedings 2013
關(guān)鍵詞evolutionary algorithms; machine learning; multi-objective optimization; scheduling problems; semantic g
版次1
doihttps://doi.org/10.1007/978-3-642-37207-0
isbn_softcover978-3-642-37206-3
isbn_ebook978-3-642-37207-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

書(shū)目名稱(chēng)Genetic Programming影響因子(影響力)




書(shū)目名稱(chēng)Genetic Programming影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Genetic Programming網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Genetic Programming網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Genetic Programming被引頻次




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Das Wichtigste zu WORD 5.5 unter MS-DOS 5.0e neighbourhood search (VNS) framework. In particular two components are generated; constructive heuristics to create initial solutions and neighbourhood move operators to change the state of a given solution. The proposed method is tested on standard benchmark instances of two common VRP variants.
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Searching for Novel Classifiersmance from two different perspectives. Experimental results show that NS-based search can be used to derive effective classifiers. In particular, NS is best suited to solve difficult problems, where exploration needs to be encouraged and maintained.
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A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classificationmpared to other multi-label classification techniques in different application domains. Computational experiments show that G3P-ML often obtains better results than other algorithms while achieving a lower number of rules than the other methods.
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