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Foraging-inspired optimisation algor...

Brabazon, Anthony.

Foraging-inspired optimisation algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Foraging-inspired optimisation algorithmsby Anthony Brabazon, Sean McGarraghy.
Author:	Brabazon, Anthony.
other author:	McGarraghy, Sean.
Published:	Cham :Springer International Publishing :2018.
Description:	xviii, 478 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-3-319-59156-8
ISBN:	9783319591568$q(electronic bk.)

Foraging-inspired optimisation algorithms
Brabazon, Anthony.

Foraging-inspired optimisation algorithms[electronic resource] /by Anthony Brabazon, Sean McGarraghy. - Cham :Springer International Publishing :2018. - xviii, 478 p. :ill., digital ;24 cm. - Natural computing series,1619-7127. - Natural computing series..

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.

ISBN: 9783319591568$q(electronic bk.)

Standard No.: 10.1007/978-3-319-59156-8doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Foraging-inspired optimisation algorithms
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245 1 0 $a Foraging-inspired optimisation algorithms $h [electronic resource] / $c by Anthony Brabazon, Sean McGarraghy.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xviii, 478 p. : $b ill., digital ; $c 24 cm.
490 1 $a Natural computing series, $x 1619-7127
505 0 $a 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.
520 $a 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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700 1 $a McGarraghy, Sean. $3 731246
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