A framework for self-tuning optimization algorithm

@article{Yang2013AFF,
  title={A framework for self-tuning optimization algorithm},
  author={X. Yang and S. Deb and M. Loomes and M. Karamanoglu},
  journal={Neural Computing and Applications},
  year={2013},
  volume={23},
  pages={2051-2057}
}
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… Expand
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