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The Nature of Nature: Why Nature-Inspired Algorithms Work

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Nature-Inspired Computing and Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 10))

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 neighbourhoods 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.

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Acknowledgements

This research was supported under Australian Research Council’s Discovery Projects funding scheme, project number DE 140100017.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, 3800, Australia

    David Green, Aldeida Aleti & Julian Garcia

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  1. David Green
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  2. Aldeida Aleti
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  3. Julian Garcia
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Correspondence to Aldeida Aleti .

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Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

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Green, D., Aleti, A., Garcia, J. (2017). The Nature of Nature: Why Nature-Inspired Algorithms Work. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-50920-4_1

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