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Titlebook: Evolutionary Algorithms, Swarm Dynamics and Complex Networks; Methodology, Perspec Ivan Zelinka,Guanrong Chen Book 2018 Springer-Verlag Gmb

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書目名稱Evolutionary Algorithms, Swarm Dynamics and Complex Networks
副標(biāo)題Methodology, Perspec
編輯Ivan Zelinka,Guanrong Chen
視頻videohttp://file.papertrans.cn/318/317818/317818.mp4
概述Includes recent research in Complex Networks and Evolutionary Dynamics.Highlights the mutual relations between the dynamics of evolutionary algorithms, complex networks, and CML (Coupled Map Lattices)
叢書名稱Emergence, Complexity and Computation
圖書封面Titlebook: Evolutionary Algorithms, Swarm Dynamics and Complex Networks; Methodology, Perspec Ivan Zelinka,Guanrong Chen Book 2018 Springer-Verlag Gmb
描述.Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), whichare usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the
出版日期Book 2018
關(guān)鍵詞Complex Networks; Coupled Map Lattices; Evolutionary Algorithms; Evolutionary Dynamics; spatiotemporal D
版次1
doihttps://doi.org/10.1007/978-3-662-55663-4
isbn_softcover978-3-662-57247-4
isbn_ebook978-3-662-55663-4Series ISSN 2194-7287 Series E-ISSN 2194-7295
issn_series 2194-7287
copyrightSpringer-Verlag GmbH Germany 2018
The information of publication is updating

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Sandro Longo,Maria Giovanna Tandaent trend of adaptive and learning methods for improving the performance of evolutionary computational techniques. It seems very likely that the complex network and its statistical characteristics can be used within those adaptive approaches. The network analysis also provides usefull insight into t
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Eskalation und Deeskalation von Commitments on the experimental investigations on the time development and influence of different randomization types, different strategies for Differential Evolution (DE) through the analysis of complex network as a record of population dynamics and indices selection. The population is visualized as an evolvi
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