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Nature-Inspired Computation: An Unconventional Approach to Optimization

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Advances in Unconventional Computing

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 23))

Abstract

Nature-inspired computation plays an increasingly important role in many areas such as computational intelligence, optimization and data mining. From the perspective of traditional algorithms, such nature-inspired, iterative problem-solving methods are an unconventional approach to optimization. Both the number of algorithms and the popularity have increased significantly in recent years. This chapter provides a critical analysis of some nature-inspired algorithms and strives to identify the most essential characteristics among these algorithms. We also look at different algorithmic structures and ways of generating new solutions in a mathematical framework, which will provide some insight into these algorithms. We also discuss some key open problems concerning nature-inspired metaheuristics.

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Authors and Affiliations

  1. School of Science and Technology, Middlesex University London, London, NW4 4BT, UK

    Xin-She Yang

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  1. Xin-She Yang
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Correspondence to Xin-She Yang .

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  1. Unconventional ComputingCentre, University of the Westof England Unconventional ComputingCentre, Bristol, United Kingdom

    Andrew Adamatzky

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Yang, XS. (2017). Nature-Inspired Computation: An Unconventional Approach to Optimization. In: Adamatzky, A. (eds) Advances in Unconventional Computing. Emergence, Complexity and Computation, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-33921-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-33921-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33920-7

  • Online ISBN: 978-3-319-33921-4

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