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Metaheuristics for Data Clustering and Image Segmentation / by Meera Ramadas, Ajith Abraham.

By: Ramadas, Meera, autor
Contributor(s): SpringerLink (Online service) | Abraham, Ajith (1968-), autor
Series: (Intelligent Systems Reference Library, 1868-4394; 152); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Cham : Imprint: Springer, 2019Description: 1 recurso en línea (IX, 163 páginas).ISBN: 9783030040970.Subject: Algoritmos computacionalesOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- METAHEURISTICS AND DATA CLUSTERING -- REVISED MUTATION STRATEGY FOR DIFFERENTIAL EVOLUTION ALGORITHM -- SEARCH strategy Flower Pollination Algorithm with Differential Evolution. .
Abstract: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.9.A43 2019 EB (Browse shelf(Opens below)) Acceso electrónico eBooks26062278
Total holds: 0

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.

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