A framework for self-tuning optimization algorithm

  title={A framework for self-tuning optimization algorithm},
  author={Xin-She Yang and Suash Deb and Martin Loomes and Mehmet Karamanoğlu},
  journal={Neural Computing and Applications},
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… 

12 A Framework for Self-Tuning Algorithms

  • Computer Science
  • 2014
This chapter presents a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself.

A Framework for Self-Tuning Algorithms

  • Xin-She Yang
  • Computer Science
    Nature-Inspired Optimization Algorithms
  • 2021

A self-tuned bat algorithm for optimization in radiation therapy treatment planning

  • G. KalantzisY. Lei
  • Computer Science
    15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
  • 2014
The proposed Self-Tuned Bat Algorithm (STBA) finds itself the optimal set of algorithm-dependent parameters and therefore minimizes the number of iterations required for the optimization to reach sub-optimal solution.

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Nature-Inspired Optimization Algorithms: Challenges and Open Problems




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