Overview
- Understanding foraging strategies improves search processes
- No prior knowledge of natural computing assumed
- Valuable for graduate students, academics and practitioners in computer science, informatics, data science, and management science
Part of the book series: Natural Computing Series (NCS)
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Table of contents (19 chapters)
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Front Matter
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Perspectives on Foraging
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Front Matter
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Foraging-Inspired Algorithms for Optimisation
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Front Matter
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Vertebrates
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Front Matter
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Nonneuronal Organisms
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Front Matter
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Keywords
- Foraging
- Social Learning
- Foraging Algorithms
- Animal Behavior
- Ant Foraging Algorithm
- Bioluminescence Algorithms
- Bacteria Inspired Algorithms
- Chemotaxis
- Optimization
- Search
- Learning
- Natural Computing
- Genetic Programming
- Evolutionary Computing
- Slime Mould
- Plant Foraging
- Group Search
- Predatory Search
- Heuristics
- Honeybees
About this book
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.
Authors and Affiliations
About the authors
Prof. Anthony Brabazon is currently Dean of the UCD College of Business. Previous positions held in UCD include Associate Dean and Director of the Smurfit Graduate School of Business, Vice-Principal of Research and Innovation for the College of Business and Law, and Head of Research for the School of Business. His primary research interests concern the development of natural computing theory and the application of related algorithms to real-world problems, particularly in the domain of business and finance, and he has pioneered multidisciplinary collaborations with industry in areas such as financial mathematics, financial economics, and computer science. He is cofounder and codirector of the Natural Computing Research and Applications Group at UCD, among the most successful research groups dedicated to this subject. Among his publications are the successful coauthored books 'Natural Computing Algorithms', 'Foundations in Grammatical Evolution for DynamicEnvironments', and 'Biologically Inspired Algorithms for Financial Modelling'.
Dr. Seán McGarraghy is the director of the UCD Centre for Business Analytics, he was formerly director of the UCD Smurfit Graduate School of Business MSc in Business Analytics. He has qualifications in electronics, mathematics and management and his teaching and academic publications cover many aspects of business analytics and operations research. Particular topics of interests include combinatorial enumeration and optimization, network algorithms, supply chain management, quadratic forms and K-theory. Among his publications are the successful coauthored book 'Natural Computing Algorithms'.
Bibliographic Information
Book Title: Foraging-Inspired Optimisation Algorithms
Authors: Anthony Brabazon, Seán McGarraghy
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/978-3-319-59156-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-59155-1Published: 06 October 2018
Softcover ISBN: 978-3-030-09640-3Published: 11 February 2019
eBook ISBN: 978-3-319-59156-8Published: 26 September 2018
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XVIII, 478
Topics: Theory of Computation, Computational Intelligence, Artificial Intelligence, Operations Research, Management Science, Operations Research/Decision Theory