Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
This addresses synchronization control in networked dynamical systems, with applications in engineering and medicine, but it is incremental as it applies a known method to a specific domain.
The paper tackled the problem of controlling synchronization in networks of coupled oscillators by formulating it as an optimization problem and using a multi-objective genetic programming-based approach to infer optimal control functions, resulting in highly-effective and interpretable functions for systems ranging from simple to hierarchical networks.
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 optimization 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.