Multi-Objective Evolutionary Algorithm for the Identification of Rare Functional Dependencies in Complex Technical Infrastructures

Antonello, Federico; Baraldi, Piero; Zio, Enrico; Serio, L.

doi:10.3850/978-981-14-8593-0_5862-cd

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(4 citation statements)

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“…The present work extends the MOEA proposed in Antonello et al (2020b) to address the above-mentioned issues. To this aim, the recently proposed metric of dependency (Antonello et al 2021c), which has been shown to discriminate rules with spurious alarms from rules describing actual FDEPs (Antonello et al 2021c), is used as fitness function within the MOEA search.…”

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confidence: 87%

“…When one-bit genes are employed, the initialization of the population using sub-optimal chromosomes characterized by a limited number of one-bit genes equal to 1 can facilitate the GA convergence, as shown in the context of features selection problems (Baraldi et al 2016) and association rules identification (Del Jesus et al 2011;Antonello et al 2020b). In this work, the initial population of chromosomes is created considering all the possible patterns, X ′ , made of 2 alarms ( |X � | = 2 ) which verify Eq.…”

Section: Moea Algorithmmentioning

confidence: 99%

“…As suggested by Del Jesus et al (2011), Mukhopadhyay et al (2014) in case of standard binary chromosomes, standard genetic operators and an evolution algorithm based on a traditional GA with two-point crossover and flip mutation are used. Furthermore, we avoid the presence of identical chromosomes in the population to favour population diversity (Antonello et al 2020b).…”

Section: Moea Algorithmmentioning

confidence: 99%

“…The novelty search drives the selective pressure to favour diversification in the population by dynamically rewarding the chromosomes based on their novelty with respect to other chromosomes, instead of rewarding them considering static fitness functions (Gomes et al 2017). While the approach allows the identification of rare FDEPs in CTIs Antonello et al (2021d), the following issues still need to be resolved:…”

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confidence: 99%

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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

et al. 2022

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In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

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confidence: 87%