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.
| 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 | |
| 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
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
TY - CHAP
T1 - The nature of nature
T2 - Why nature-inspired algorithms work
AU - Green, David
AU - Aleti, Aldeida
AU - Garcia, Julian
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Dual-phase evolution
KW - Evolutionary dynamics
KW - Generalized local search machines
KW - Nature-inspired algorithms
UR - http://www.scopus.com/inward/record.url?scp=85014913945&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-50920-4_1
DO - 10.1007/978-3-319-50920-4_1
M3 - Chapter (Book)
SN - 9783319509198
T3 - Modeling and Optimization in Science and Technologies
SP - 1
EP - 27
BT - Nature-Inspired Computing and Optimization
A2 - Patnaik, Srikanta
A2 - Yang, Xin-She
A2 - Nakamatsu, Kazumi
PB - Springer
CY - Cham, Switzerland
ER -