Abstract
In a previous work, Villani et al. introduced a method to identify candidate emergent dynamical structures in complex systems. Such a method detects subsets (clusters) of the system elements which behave in a coherent and coordinated way while loosely interacting with the remainder of the system. Such clusters are assessed in terms of an index that can be associated to each subset, called Dynamical Cluster Index (DCI). When large systems are analyzed, the “curse of dimensionality” makes it impossible to compute the DCI for every possible cluster, even using massively parallel hardware such as GPUs.
In this paper, we propose an efficient metaheuristic for searching relevant dynamical structures, which hybridizes an evolutionary algorithm with local search and obtains results comparable to an exhaustive search in a much shorter time. The effectiveness of the method we propose has been evaluated on a set of Boolean models of real-world systems.
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Notes
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In one of the 10 runs, HyReSS failed to detect 1 of the first 50 RSs detected by the exhaustive search.
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Acknowledgments
The authors thank the UE project “MD – Emergence by Design”, Pr.ref. 284625 7th FWP-FET program for providing the data, which where in turn kindly provided by the Green Community project, sponsored by the National Association for Municipalities and Mountain Communities (UNCEM).
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Sani, L. et al. (2016). Efficient Search of Relevant Structures in Complex Systems. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_4
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