Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Unpublished{gout2016,
  author =       "Julien Gout and Markus Quade and Kamran Shafi and 
                 Robert K. Niven and Markus Abel",
  title =        "Learning Optimal Control of Synchronization in
                 Networks of Coupled Oscillators using Genetic
                 Programming-based Symbolic Regression",
  howpublished = "arXiv:1612.05276",
  year =         "2016",
  month =        "15 " # dec,
  note =         "Submitted to nonlinear dynamics",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2017-06-07",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/corr/corr1612.html#GoutQSNA16",
  URL =          "http://arxiv.org/abs/1612.05276",
  size =         "44 pages",
  abstract =     "Networks of coupled dynamical systems provide a
                 powerful way to model systems with enormously complex
                 dynamics, such as the human brain. Control of
                 synchronization in such networked systems has far
                 reaching applications in many domains, including
                 engineering and medicine. In this paper, we formulate
                 the synchronization control in dynamical systems as an
                 optimisation problem and present a multi-objective
                 genetic programming-based approach to infer optimal
                 control functions that drive the system from a
                 synchronized to a non-synchronized state and
                 vice-versa. The genetic programming-based controller
                 allows learning optimal control functions in an
                 interpretable symbolic form. The effectiveness of the
                 proposed approach is demonstrated in controlling
                 synchronization in coupled oscillator systems linked in
                 networks of increasing order complexity, ranging from a
                 simple coupled oscillator system to a hierarchical
                 network of coupled oscillators. The results show that
                 the proposed method can learn highly-effective and
                 interpretable control functions for such systems.",
  notes =        "Also known as \cite{journals/corr/GoutQSNA16}",
}

Genetic Programming entries for Julien Gout Markus Quade Kamran Shafi Robert K Niven Markus W Abel

Citations