Evolutionary Algorithms, Swarm Dynamics and Complex Networks

Methodology, Perspectives and Implementation

  • Ivan Zelinka
  • Guanrong Chen

Part of the Emergence, Complexity and Computation book series (ECC, volume 26)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Theory

    1. Front Matter
      Pages 1-1
    2. Orkhan Yarakhmedov, Victor Polyakh, Ivan Chernogorov, Ivan Zelinka
      Pages 31-63
  3. Applications

    1. Front Matter
      Pages 65-65
    2. Lenka Skanderová, Ivan Zelinka
      Pages 67-100
    3. Lukáš Tomaszek, Ivan Zelinka
      Pages 101-114
    4. Lukáš Tomaszek, Ivan Zelinka
      Pages 115-129
    5. Lukáš Tomaszek, Ivan Zelinka
      Pages 131-143
    6. Michal Pluhacek, Roman Šenkeřík, Adam Viktorin, Tomas Kadavy
      Pages 145-159
    7. Roman Šenkeřík, Ivan Zelinka, Michal Pluhacek, Adam Viktorin, Jakub Janostik, Zuzana Kominkova Oplatkova
      Pages 177-194
    8. Ivan Zelinka, Roman Šenkeřík, Michal Pluháček
      Pages 195-210
  4. Miscellanies

    1. Front Matter
      Pages 211-211
    2. Lubomir Sikora, Ivan Zelinka
      Pages 213-239
    3. Genaro J. Martínez, Andrew Adamatzky, Bo Chen, Fangyue Chen, Juan C. Seck-Tuoh-Mora
      Pages 241-264
    4. Zhi-Hong Guan, Guang Ling
      Pages 285-309
    5. Ivan Zelinka
      Pages 311-312

About this book


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. 


Complex Networks Coupled Map Lattices Evolutionary Algorithms Evolutionary Dynamics spatiotemporal Deterministic Chaos

Editors and affiliations

  • Ivan Zelinka
    • 1
  • Guanrong Chen
    • 2
  1. 1.Department of Computer ScienceFaculty of Electrical Engineering and Computer Science VŠB-TUOOstrava, PorubaCzech Republic
  2. 2.Department of Electronic EngineeringCity University of Hong KongKowloon, Hong KongChina

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag GmbH Germany 2018
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-662-55661-0
  • Online ISBN 978-3-662-55663-4
  • Series Print ISSN 2194-7287
  • Series Online ISSN 2194-7295
  • Buy this book on publisher's site
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