Foraging-Inspired Optimisation Algorithms [electronic resource] / by Anthony Brabazon, Seán McGarraghy.

Por: Brabazon, Anthony [author.]Colaborador(es): McGarraghy, Seán [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Natural Computing SeriesEditor: Cham : Springer International Publishing : Imprint: Springer, 2018Edición: 1st ed. 2018Descripción: XVIII, 478 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319591568Tema(s): Computers | Computational intelligence | Artificial intelligence | Operations research | Management science | Decision making | Theory of Computation | Computational Intelligence | Artificial Intelligence | Operations Research, Management Science | Operations Research/Decision TheoryFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 004.0151 Clasificación LoC:QA75.5-76.95QA76.63Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Formal Models of Foraging -- Sensor Modalities -- Individual and Social Learning -- Introduction to Foraging Algorithms -- Mammals -- Bird Foraging Algorithms -- Fish Algorithms -- Ant Foraging Algorithms -- Honeybee Inspired Algorithms -- Bioluminescence Algorithms -- Spider Algorithms -- Worm Algorithm -- Bacteria Inspired Algorithms -- Slime Mould Foraging -- Plant Foraging Algorithms -- Group Search and Predatory Search -- Evolving Foraging Algorithms -- Conclusions.
En: Springer Nature eBookResumen: This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.
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Acceso multiusuario

Introduction -- Formal Models of Foraging -- Sensor Modalities -- Individual and Social Learning -- Introduction to Foraging Algorithms -- Mammals -- Bird Foraging Algorithms -- Fish Algorithms -- Ant Foraging Algorithms -- Honeybee Inspired Algorithms -- Bioluminescence Algorithms -- Spider Algorithms -- Worm Algorithm -- Bacteria Inspired Algorithms -- Slime Mould Foraging -- Plant Foraging Algorithms -- Group Search and Predatory Search -- Evolving Foraging Algorithms -- Conclusions.

This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.

UABC ; Temporal ; 01/01/2021-12/31/2023.

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