Research on the Construction of a Financial Risk Early Warning Model Based on Association Rule Algorithm

In the field of computer technology, choosing a dissertation topic is an important and complex multi-objective task for graduate students. This process entails identifying a research question, defining the scope of the research, and formulating a research problem. However, students often encounter difficulties in selecting a dissertation topic that meets their interests, abilities, and academic goals. These challenges arise from the complexity of the research area, the limited knowledge of students, and the lack of a systematic approach to topic selection. This paper explores the use of genetic algorithms (GA), a heuristic optimization technique inspired by the natural selection process, to enhance dissertation topic selection. The goal is to develop a GA-based system that assists graduate students in identifying the most appropriate dissertation topic based on their academic background, research interests, and preferences and to compare it to the usual methods of selecting dissertation topics. The results show that using genetic algorithms to select dissertation topics scores higher than manual selection , advisor guidance , and random selection.