2013/12/06 by Muhammad Marwan Muhammad Fuad, Fuad, Muhammad Marwan Muhammad
Computer Science · #Algorithms and Data Compression #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Time Series Analysis and Forecasting #Video Analysis and Summarization #cs.AI #cs.NE
paper · pdf · doi:10.48550/arxiv.1312.1760
The 8th International Conference on Advanced Data Mining and Applications (ADMA 2012)
arxiv created 2013/12/06 · openalex publication_date 2013/12/06 · arxiv updated 2013/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The normalized edit distance is one of the distances derived from the edit distance. It is useful in some applications because it takes into account the lengths of the two strings compared. The normalized edit distance is not defined in terms of edit operations but rather in terms of the edit path. In this paper we propose a new derivative of the edit distance that also takes into consideration the lengths of the two strings, but the new distance is related directly to the edit distance. The particularity of the new distance is that it uses the genetic algorithms to set the values of the parameters it uses. We conduct experiments to test the new distance and we obtain promising results.