Evolutionary Synchronization of Chaotic Systems

  • Ivan Zelinka
  • Ales Raidl
Part of the Studies in Computational Intelligence book series (SCI, volume 267)


This chapter introduces a simple investigation on deterministic chaos synchronization by means of selected evolutionary techniques. Five evolutionary algorithms has been used for chaos synchronization here: differential evolution, self-organizing migrating algorithm, genetic algorithm, simulated annealing and evolutionary strategies in a total of 15 versions. Experiments in this chapter has been done with two different coupled systems (master — slave) — Rössler-Lorenz and Lorenz-Lorenz. The main aim of this chapter was to show that evolutionary algorithms, under certain conditions, are capable of synchronization of, at least, simple chaotic systems, when the cost function is properly defined as well as the parameters of selected evolutionary algorithm. This chapter consists of two different case studies. For all algorithms each simulation was 100 times repeated to show and check the robustness of proposed methods and experiment configurations. All data were processed to obtain summarized results and graphs.


Cost Function Simulated Annealing Evolutionary Algorithm Differential Evolution Chaotic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ivan Zelinka
    • 1
    • 2
  • Ales Raidl
    • 3
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  2. 2.Faculty of Electrical Engineering and Computer ScienceVSB-TUOOstrava-PorubaCzech Republic
  3. 3.Faculty of Mathematics and PhysicsCharles UniversityPrague 8Czech Republic

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