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Titlebook: Genetic Programming; 14th European Confer Sara Silva,James A. Foster,Mario Giacobini Conference proceedings 2011 Springer Berlin Heidelberg

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書目名稱Genetic Programming
副標題14th European Confer
編輯Sara Silva,James A. Foster,Mario Giacobini
視頻videohttp://file.papertrans.cn/383/382576/382576.mp4
概述Fast track conference proceedings.Unique visibility.State of the art research
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Genetic Programming; 14th European Confer Sara Silva,James A. Foster,Mario Giacobini Conference proceedings 2011 Springer Berlin Heidelberg
描述This book constitutes the refereed proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events. .This 20 revised full papers presented together with 9 poster papers were carefully reviewed and selected from 59 submissions. The wide range of topics in this volume reflect the current state of research in the field, including representations, theory, novel operators and techniques, self organization, and applications.
出版日期Conference proceedings 2011
關(guān)鍵詞brain-computer interfaces; evolutionary programming; genetic algorithms; modeling; statistical methods; a
版次1
doihttps://doi.org/10.1007/978-3-642-20407-4
isbn_softcover978-3-642-20406-7
isbn_ebook978-3-642-20407-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2011
The information of publication is updating

書目名稱Genetic Programming影響因子(影響力)




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書目名稱Genetic Programming網(wǎng)絡(luò)公開度




書目名稱Genetic Programming網(wǎng)絡(luò)公開度學科排名




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Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learningralisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geome
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A Peer-to-Peer Approach to Genetic Programmingtralized in order to take full-advantage of a massively parallel Peer-to-Peer infrastructure. In this context, GP is particularly demanding due to its high requirements of computational power. To assess the viability of the approach, the .model has been empirically analyzed in a simulated Peer-to-Pe
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