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We consider the problem of mining signal temporal logical requirements from adataset of regular (good) and anomalous (bad) trajectories of a dynamicalsystem. We assume the training set to be labeled by human experts and that wehave access only to a limited amount of data, typically noisy. We provide asystematic approach to synthesize both the syntactical structure and theparameters of the temporal logic formula using a two-steps procedure: first, weleverage a novel evolutionary algorithm for learning the structure of theformula; second, we perform the parameter synthesis operating on thestatistical emulation of the average robustness for a candidate formula w.r.t.its parameters. We compare our results with our previous work [{BufoBSBLB14]and with a recently proposed decision-tree [bombara_decision_2016] basedmethod. We present experimental results on two case studies: an anomaloustrajectory detection problem of a naval surveillance system and thecharacterization of an Ineffective Respiratory effort, showing the usefulnessof our work.

A Robust Genetic Algorithm for Learning Temporal Specifications from Data