Efficient Search of Relevant Structures in Complex Systems

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's 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.

A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

“…For example, some PIs are in charge of elaborating students' IDs, some others the times of the day for accessing certain LOs, and so on. The PIs keep track of sufficient statistics to analyze the corresponding attributes, thus reducing memory usage and parallelizing the computation of fitness functions for split decisions, similarly to what was done in [42]. Nevertheless, clustering methods are supported as well.…”

An Iterative Information-Theoretic Approach to the Detection of Structures in Complex Systems

“…For large-sized systems, an exhaustive enumeration is obviously impractical because the number of possible subsets of set U is 2 |U| , where |U| denotes the cardinality of U. When the computation load needed for an exhaustive enumeration exceeds the available computing resources, one needs to resort either to random sampling or to metaheuristic techniques, like the genetic algorithms hybridized with a local search we use in this work [9]. The main general idea of this approach consists in performing a sampling that is biased towards sets of variables characterised by high T c values.…”

“…In the metaheuristic we developed, named HyReSS (Hybrid Relevant Set Search) [9], a genetic algorithm is first executed to address the search towards the basins of attraction of the main local maxima. Then, the results are refined through a series of local searches, driven by the statistics, computed at runtime, on the results that the algorithm is providing.…”

“…Moreover, they are distributed over time without a predefined frequency. Following an indication presented also in [9], our idea was that the simple presence or absence at important meetings (or even the mere permission to participate in them) could carry significant information about the real project profiles. Therefore, we obtained a very sparse matrix composed of 136 variables and 101 pointsof-control (observations) (see Figure 3(a)).…”

“…The RI method has been applied with interesting results to several systems: some of them had been artificially designed in order to test the effectiveness of the technique, while others referred to interesting physical, chemical, biological, or socio-economic systems [6,7]. In addition, the efficiency of the method has also been improved by using a parallel implementation of the RI computation [8] and some metaheuristics to deal with the "curse of dimensionality" when analysing high-dimensional systems [9,10]. In general, the method can be applied every time a collection of observations of the values of the system variables at different instants or conditions is available.…”