The nature of nature: Why nature-inspired algorithms work

    Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

    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 languageEnglish
    Title of host publicationNature-Inspired Computing and Optimization
    Subtitle of host publicationTheory and Applications
    EditorsSrikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu
    Place of PublicationCham, Switzerland
    PublisherSpringer
    Pages1-27
    Number of pages27
    ISBN (Electronic)9783319509204
    ISBN (Print)9783319509198
    DOIs
    Publication statusPublished - 2017

    Publication series

    NameModeling and Optimization in Science and Technologies
    PublisherSpringer
    Volume10
    ISSN (Print)2196-7326
    ISSN (Electronic)2196-7334

    Keywords

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

    Cite this

    Green, D., Aleti, A., & Garcia, J. (2017). The nature of nature: Why nature-inspired algorithms work. In S. Patnaik, X-S. Yang, & K. Nakamatsu (Eds.), Nature-Inspired Computing and Optimization: Theory and Applications (pp. 1-27). (Modeling and Optimization in Science and Technologies; Vol. 10). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-50920-4_1
    Green, David ; Aleti, Aldeida ; Garcia, Julian. / The nature of nature : Why nature-inspired algorithms work. Nature-Inspired Computing and Optimization: Theory and Applications. editor / Srikanta Patnaik ; Xin-She Yang ; Kazumi Nakamatsu. Cham, Switzerland : Springer, 2017. pp. 1-27 (Modeling and Optimization in Science and Technologies).
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    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.",
    keywords = "Dual-phase evolution, Evolutionary dynamics, Generalized local search machines, Nature-inspired algorithms",
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    Green, D, Aleti, A & Garcia, J 2017, The nature of nature: Why nature-inspired algorithms work. in S Patnaik, X-S Yang & K Nakamatsu (eds), Nature-Inspired Computing and Optimization: Theory and Applications. Modeling and Optimization in Science and Technologies, vol. 10, Springer, Cham, Switzerland, pp. 1-27. https://doi.org/10.1007/978-3-319-50920-4_1

    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 proceedingChapter (Book)Researchpeer-review

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    Green D, Aleti A, Garcia J. The nature of nature: Why nature-inspired algorithms work. In Patnaik S, Yang X-S, Nakamatsu K, editors, Nature-Inspired Computing and Optimization: Theory and Applications. Cham, Switzerland: Springer. 2017. p. 1-27. (Modeling and Optimization in Science and Technologies). https://doi.org/10.1007/978-3-319-50920-4_1