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A framework for self-tuning optimization algorithm

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Abstract

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.

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Authors and Affiliations

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

    Xin-She Yang, Martin Loomes & Mehmet Karamanoglu

  2. Cambridge Institute of Technology, Cambridge Village, Tatisilwai, Ranchi, 835103, Jharkhand, India

    Suash Deb

Authors
  1. Xin-She Yang
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  2. Suash Deb
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  3. Martin Loomes
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  4. Mehmet Karamanoglu
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Correspondence to Suash Deb.

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Yang, XS., Deb, S., Loomes, M. et al. A framework for self-tuning optimization algorithm. Neural Comput & Applic 23, 2051–2057 (2013). https://doi.org/10.1007/s00521-013-1498-4

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  • DOI: https://doi.org/10.1007/s00521-013-1498-4

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