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Titlebook: Swarm Intelligence; 10th International C Marco Dorigo,Mauro Birattari,Thomas Stützle Conference proceedings 2016 Springer International Pub

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书目名称Swarm Intelligence
副标题10th International C
编辑Marco Dorigo,Mauro Birattari,Thomas Stützle
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Swarm Intelligence; 10th International C Marco Dorigo,Mauro Birattari,Thomas Stützle Conference proceedings 2016 Springer International Pub
描述.This book constitutes the proceedings of the 10th International Conference on Swarm Intelligence, ANTS 2016, held in Brussels, Belgium, in September 2016. . The 18 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 47 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions. .
出版日期Conference proceedings 2016
关键词bio-inspired optimization; discrete optimization; evolutionary robotics; mixed discrete-continuous opti
版次1
doihttps://doi.org/10.1007/978-3-319-44427-7
isbn_softcover978-3-319-44426-0
isbn_ebook978-3-319-44427-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Design and Analysis of Proximate Mechanisms for Cooperative Transport in Real Robotsconditions in which we vary physical characteristics of the object and group cardinality. From a biological perspective, the results of this study indicate that the perception of the object movement could explain how natural organisms manage to coordinate their actions to transport heavy items.
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Synthesizing Rulesets for Programmable Robotic Self-assembly: A Case Study Using Floating Miniaturizn addition, our method allows for a reduced complexity in the rulesets. In order to illustrate the application of our method, we extend two synthesis algorithms from the literature, namely Singleton and Linchpin, to synthesize rules applicable to our floating robots. A microscopic simulation framewo
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Using Ant Colony Optimization to Build Cluster-Based Classification Systemsvaluate performance on 30 UCI benchmark datasets. We compare the ACO results to the traditional .-means clustering algorithm, where the data clusters are built prior to learning the local classifiers.
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