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Erschienen in:
Complexity Decomplexified: A List of 200+ Results Encountered Over 55 Years Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Controlling Complexity

verfasst von : Ivan Zelinka, Petr Saloun, Roman Senkerik, Michal Pavelch

Erschienen in: How Nature Works

Verlag: Springer International Publishing

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Abstract

Complex systems and dynamics are present in many parts of daily life and branches of science. This chapter is continuation of our previous research, that introduced a novelty method of visualization and possible control of complex networks, that are used to visualize dynamics of evolutionary algorithms. Selected evolutionary algorithms are used as an example in order to show how its behavior can be understood as complex network and controlled via conversion into CML system—a model based on mutually joined nonlinear n equations. The main aim of this investigation was to show that dynamics of evolutionary algorithms can be converted via complex network to CML system and then controlled. Selected results of conversion of evolutionary dynamics into complex network and consequently to CML as well as controlled CML system are discussed here.

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Vorheriges Kapitel Computational Tactic to Retrieve a Complex Seismic Structure of the Hydrocarbon Model
Nächstes Kapitel Influence of Chaotic Dynamics on the Performance of Differential Evolution Algorithm
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Metadaten
Titel
Controlling Complexity
verfasst von
Ivan Zelinka
Petr Saloun
Roman Senkerik
Michal Pavelch
Copyright-Jahr
2014
Verlag
Springer International Publishing
Buch
How Nature Works

Print ISBN: 978-3-319-00253-8

Electronic ISBN: 978-3-319-00254-5

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-00254-5_11