University of Twente Student Theses
Understanding the application of Multi-Objective Evolutionary Algorithms to the Inference of Fault Tree Models
Colța, B. (2024) Understanding the application of Multi-Objective Evolutionary Algorithms to the Inference of Fault Tree Models.
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| Abstract: | Fault Tree Analysis (FTA) is a recognized method in reliability engineering and risk assessment that manages systems by providing a structured depiction of how failures propagate and offering quantitative and qualitative metrics. Several challenges associated with FTA relate to model construction, which can be time-consuming and error-prone. Several algorithms have been proposed to address this for the automatic inference of Fault Trees. Within the state-of-the-art algorithms is FT-MOEA, which utilizes multi-objective evolutionary algorithms to construct compact and efficient Fault Tree structures from failure datasets. However, a significant challenge FT-MOEA faces relates to scalability. The goal of this research is to further focus on investigating the influence of genetic operators on the convergence of the algorithm. The paper proposes an extension to the algorithm's implementation that analyzes each step of the evolution process by collecting the metrics of each FT obtained and the genetic operators applied. Moreover, the paper suggests some analysis metrics that describe the performance and efficiency of genetic operators. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/100965 |
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