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Evolutionary algorithms, swarm dynam...

Zelinka, Ivan.

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Evolutionary algorithms, swarm dynamics and complex networks = methodology, perspectives and implementation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary algorithms, swarm dynamics and complex networks/ edited by Ivan Zelinka, Guanrong Chen.
Reminder of title:	methodology, perspectives and implementation /
other author:	Zelinka, Ivan.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2018.,
Description:	xxii, 312 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Evolutionary computation. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-55663-4
ISBN:	9783662556634

Evolutionary algorithms, swarm dynamics and complex networks = methodology, perspectives and implementation /
Evolutionary algorithms, swarm dynamics and complex networksmethodology, perspectives and implementation /[electronic resource] :edited by Ivan Zelinka, Guanrong Chen. - Berlin, Heidelberg :Springer Berlin Heidelberg :2018. - xxii, 312 p. :ill., digital ;24 cm. - Emergence, complexity and computation,v.262194-7287 ;. - Emergence, complexity and computation ;v.26..

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), which are 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 best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.

ISBN: 9783662556634

Standard No.: 10.1007/978-3-662-55663-4doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: TA347.E96

Dewey Class. No.: 006.3823

Evolutionary algorithms, swarm dynamics and complex networks = methodology, perspectives and implementation /
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245 0 0 $a Evolutionary algorithms, swarm dynamics and complex networks $h [electronic resource] : $b methodology, perspectives and implementation / $c edited by Ivan Zelinka, Guanrong Chen.
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300 $a xxii, 312 p. : $b ill., digital ; $c 24 cm.
490 1 $a Emergence, complexity and computation, $x 2194-7287 ; $v v.26
520 $a 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), which are 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 best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
650 0 $a Evolutionary computation. $3 582189
650 0 $a Swarm intelligence. $3 577800
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650 2 4 $a Complexity. $3 893807
650 2 4 $a Applications of Graph Theory and Complex Networks. $3 3134760
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