Klaipėdos universitetas

The article examines the crucial role of innovative analytical and statistical technology in electoral forensics, which are increasingly used for detecting and preventing electoral corruption and fraud. By analysing vast amounts of data and detecting anomalies, electoral forensic investigations can contribute to fair and transparent democratic processes. The research aims to explore the effectiveness of these technologies and their potential impact on improving the transparency and fairness of electoral processes, using a multi-method approach that includes analysing relevant documents, media coverage, public opinion, and recent fraud cases. The authors divide the implementation of innovative analytical and statistical technologies for combating election corruption into four groups. The first is the analysis of statistical data and research on corruption, including election processes, which can be called secondary data analysis. The second is the analysis of documentary data containing information on corrupt actions and offences, including election processes. The third is the development of mathematical methods and algorithms using cutting-edge technologies such as artificial intelligence and machine learning for detecting anomalies and hidden patterns. The fourth is experimental developments in information technologies as a means of ensuring proper governance and combating corruption. While the use of algorithms for detecting anomalies in electoral statistics data can be an important tool, it should be used with caution, and in combination with other sources of information, to avoid the consequences of delegitimising the election results.

Population initialization is one of the important tasks in evolutionary and genetic algorithms (GAs). It can affect considerably the speed of convergence and the quality of the obtained results. In this paper, some heuristic strategies (procedures) for construction of the initial populations in genetic algorithms are investigated. The purpose is to try to see how the different population initialization strategies (procedures) can influence the quality of the final solutions of GAs. Several simple procedures were algorithmically implemented and tested on one of the hard combinatorial optimization problems, the quadratic assignment problem (QAP). The results of the computational experiments demonstrate the usefulness of the proposed strategies. In addition, these strategies are of quite general character and may be easily transferred to other population-based metaheuristics (like particle swarm or bee colony optimization methods).