Metaheuristics for Data Clustering and Image Segmentation

• Κύριοι συγγραφείς: Ramadas, Meera (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Abraham, Ajith (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Intelligent Systems Reference Library, 152
• Θέματα: Computational intelligence.; Artificial intelligence.; Optical data processing.; Algorithms.; Computational Intelligence.; Artificial Intelligence.; Image Processing and Computer Vision.
• Φυσική περιγραφή: IX, 163 p. 78 illus., 59 illus. in color. online resource.
• ISBN: 9783030040970
• ISSN: 1868-4394 ;
• DOI: 10.1007/978-3-030-04097-0

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.