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Foraging-Inspired Optimisation Algorithms
Author
Title
Foraging-Inspired Optimisation Algorithms [electronic resource] / by Anthony Brabazon, Seán McGarraghy.
ISBN
9783319591568
Edition
1st ed. 2018.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2018.
Physical Description
1 online resource (XVIII, 478 p.)
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
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Printed edition:
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Format
Books / Online
Language
English
Added to Catalog
July 26, 2023
Series
Natural Computing Series,
Contents
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.
Subjects
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