The nature of nature: Why nature-inspired algorithms work

David Green; Aldeida Aleti; Julian Garcia

doi:10.1007/978-3-319-50920-4_1

The nature of nature: Why nature-inspired algorithms work

David Green, Aldeida Aleti, Julian Garcia

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

7 Citations (Scopus)

Abstract

Nature has inspired many algorithms for solving complex problems. Understanding how and why these natural models work leads not only to new insights about nature, but also to an understanding of deep relationships between familiar algorithms. Here, we show that network properties underlie and define a whole family of nature-inspired algorithms. In particular, the network defined by neighbor hoods within landscapes (real or virtual) underlies the searches and phase transitions mediate between local and global search. Three paradigms drawn from computer science—dual-phase evolution, evolutionary dynamics and generalized local search machines—provide theoretical foundations for understanding how nature-inspired algorithms function. Several algorithms provide useful examples, especially genetic algorithms, ant colony optimization and simulated annealing.

Original language	English
Title of host publication	Nature-Inspired Computing and Optimization
Subtitle of host publication	Theory and Applications
Editors	Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	1-27
Number of pages	27
ISBN (Electronic)	9783319509204
ISBN (Print)	9783319509198
DOIs	https://doi.org/10.1007/978-3-319-50920-4_1
Publication status	Published - 2017

Publication series

Name	Modeling and Optimization in Science and Technologies
Publisher	Springer
Volume	10
ISSN (Print)	2196-7326
ISSN (Electronic)	2196-7334

Keywords

Dual-phase evolution
Evolutionary dynamics
Generalized local search machines
Nature-inspired algorithms

Access to Document

10.1007/978-3-319-50920-4_1

Cite this

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The nature of nature: Why nature-inspired algorithms work. / Green, David; Aleti, Aldeida ; Garcia, Julian.
Nature-Inspired Computing and Optimization: Theory and Applications. ed. / Srikanta Patnaik; Xin-She Yang; Kazumi Nakamatsu. Cham, Switzerland: Springer, 2017. p. 1-27 (Modeling and Optimization in Science and Technologies; Vol. 10).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

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The nature of nature: Why nature-inspired algorithms work

Abstract

Publication series

Keywords

Access to Document

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Adaptive Optimisation of Complex Combinatorial Problems

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The nature of nature: Why nature-inspired algorithms work

Abstract

Publication series

Keywords

Access to Document

Other files and links

Projects

Adaptive Optimisation of Complex Combinatorial Problems

Cite this