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Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization

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Nature-Inspired Computation in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 637))

Abstract

Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggressive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics.

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Author information

Authors and Affiliations

  1. LRIT, Associated Unit to the CNRST (URAC 29), Mohammed V-Agdal University, B.P. 1014, Rabat, Morocco

    Aziz Ouaarab

  2. School of Science and Technology, Middlesex University, The Burroughs, London, NW4 4BT, UK

    Xin-She Yang

Authors
  1. Aziz Ouaarab
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  2. Xin-She Yang
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Correspondence to Aziz Ouaarab .

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Editors and Affiliations

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

    Xin-She Yang

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Ouaarab, A., Yang, XS. (2016). Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-30235-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30233-1

  • Online ISBN: 978-3-319-30235-5

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