Self-Organizing Migrating Algorithm : Methodology and Implementation / edited by Donald Davendra, Ivan Zelinka
Contributor(s): SpringerLink (Online service)
| Davendra, Donald, editor literario | Zelinka, Ivan., editor literario
Material type: 
E-bookSeries: (Studies in Computational Intelligence, 1860-949X; 626).Publisher: Cham : Springer International Publishing, 2016Edition: 1st ed.Description: 1 recurso en línea (XVIII, 289 páginas) : 128 ilustraciones, 41 ilustraciones en color.ISBN: 9783319281612.Subject: Inteligencia artificial | Optimización matemáticaDDC classification: 006.3 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
Abstract: This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA. .
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | Q342 .S454 2016 EB (Browse shelf(Opens below)) | .i1159066x | Acceso electrónico | eBOOK .i1159066x |
This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA. .
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