Metaheuristics for Data Clustering and Image Segmentation
Ramadas, Meera
Metaheuristics for Data Clustering and Image Segmentation by Meera Ramadas, Ajith Abraham. - 1 recurso en línea (IX, 163 páginas) - Intelligent Systems Reference Library 152 1868-4394 Intelligent Technologies and Robotics (Springer-42732) .
Introduction -- METAHEURISTICS AND DATA CLUSTERING -- REVISED MUTATION STRATEGY FOR DIFFERENTIAL EVOLUTION ALGORITHM -- SEARCH strategy Flower Pollination Algorithm with Differential Evolution. .
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.
9783030040970
10.1007/978-3-030-04097-0 doi
Algoritmos computacionales
QA76.9.A43 / 2019 EB
Metaheuristics for Data Clustering and Image Segmentation by Meera Ramadas, Ajith Abraham. - 1 recurso en línea (IX, 163 páginas) - Intelligent Systems Reference Library 152 1868-4394 Intelligent Technologies and Robotics (Springer-42732) .
Introduction -- METAHEURISTICS AND DATA CLUSTERING -- REVISED MUTATION STRATEGY FOR DIFFERENTIAL EVOLUTION ALGORITHM -- SEARCH strategy Flower Pollination Algorithm with Differential Evolution. .
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.
9783030040970
10.1007/978-3-030-04097-0 doi
Algoritmos computacionales
QA76.9.A43 / 2019 EB