Sinyal zamansal mantık formüllerinin sentezlenmesinde genetik algoritmaların kullanımı.

Signal Temporal Logic (STL) is used to reason about the behavior of continuous signals. Due to its expressivity and algorithms to generate signal monitors, it is used to define monitoring rules for hardware and software systems. However, it is a hard task to define an STL formula that describes the system behavior. An expert of the monitored system should write the formula and optimize its parameters to minimize the monitoring errors (false alarms and missed alarms). To simplify and disseminate the use of formal monitors, its necessary to automate the formula writing process, which motivates this study. In this dissertation, we study the problem of synthesizing STL formulas from a labeled set of system traces. The datasets from different fields may vary in size and can be quite large. In order to handle such cases, we developed a parallel formula synthesis method based on genetic algorithms. The proposed method does not require any expertise guidance; it generates formula structure and optimizes the formula parameters simultaneously. In addition, in order to find the local optimum around an individual, we developed a randomized search over the parameter space, which is shown to speed up the convergence of the proposed method.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering