Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Created by W.Langdon from
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- @Unpublished{gout2016,
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author = "Julien Gout and Markus Quade and Kamran Shafi and
Robert K. Niven and Markus Abel",
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title = "Learning Optimal Control of Synchronization in
Networks of Coupled Oscillators using Genetic
Programming-based Symbolic Regression",
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howpublished = "arXiv:1612.05276",
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year = "2016",
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month = "15 " # dec,
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note = "Submitted to nonlinear dynamics",
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keywords = "genetic algorithms, genetic programming",
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bibdate = "2017-06-07",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/corr/corr1612.html#GoutQSNA16",
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URL = "http://arxiv.org/abs/1612.05276",
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size = "44 pages",
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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.",
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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