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Controlling Complexity

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How Nature Works

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 5))

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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Acknowledgments

The following two grants are acknowledged for the financial support provided for this research: Grant Agency of the Czech Republic—GACR 13-08195S, by the Development of human resources in research and development of latest soft computing methods and their application in practice project, reg. no. CZ.1.07/2.3.00/20.0072 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic.

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Authors and Affiliations

  1. Department of Computing Science, Faculty of Electrical Engineering and Computing Science, Technical University of Ostrava, Tr. 17. Listopadu 15, Ostrava, Czech Republic

    Ivan Zelinka & Petr Saloun

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 76001, Zlin, Czech Republic

    Roman Senkerik & Michal Pavelch

Authors
  1. Ivan Zelinka
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  2. Petr Saloun
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  3. Roman Senkerik
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  4. Michal Pavelch
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Correspondence to Ivan Zelinka .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and, VÅ B-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Ivan Zelinka

  2. , Institute for Theoretical Physics, University of Tübingen, Room D8 A32, Auf der Morgenstelle 14, Tübingen, 72076, Germany

    Ali Sanayei

  3. Kroto Research Institute, Department of Computer Science, The University of Sheffield, Regent Court, 211, Portobello, S1 4DP, United Kingdom

    Hector Zenil

  4. , Institute for Physical and, University of Tübingen, Auf der Morgenstelle 8, Tübingen, 72076, Germany

    Otto E. Rössler

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Zelinka, I., Saloun, P., Senkerik, R., Pavelch, M. (2014). Controlling Complexity. In: Zelinka, I., Sanayei, A., Zenil, H., Rössler, O. (eds) How Nature Works. Emergence, Complexity and Computation, vol 5. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00254-5_11

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

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