Self-adaptive lower confidence bound: A new general and effective prescreening method for Gaussian Process surrogate model assisted evolutionary algorithms

@article{Liu2012SelfadaptiveLC,
  title={Self-adaptive lower confidence bound: A new general and effective prescreening method for Gaussian Process surrogate model assisted evolutionary algorithms},
  author={Bo Liu and Qingfu Zhang and Francisco V. Fern{\'a}ndez and Georges G. E. Gielen},
  journal={2012 IEEE Congress on Evolutionary Computation},
  year={2012},
  pages={1-6}
}
Surrogate model assisted evolutionary algorithms are receiving much attention for the solution of optimization problems with computationally expensive function evaluations. For small scale problems, the use of a Gaussian Process surrogate model and prescreening methods has proven to be effective. However, each commonly used prescreening method is only suitable for some types of problems, and the proper prescreening method for an unknown problem cannot be stated beforehand. In this paper, the… CONTINUE READING
4 Citations
15 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 15 references

Differential Evolution. A Practical Approach to Global Optimization

  • K Price
  • Differential Evolution. A Practical Approach to…
  • 2005
1 Excerpt

Parasitic-Aware Optimization of CMOS RF Circuits

  • D Allstot
  • Parasitic-Aware Optimization of CMOS RF Circuits
  • 2003
1 Excerpt

The design and analysis of computer experiments

  • T J Santner
  • The design and analysis of computer experiments
  • 2003
2 Excerpts

A framework for evolutionary optimization with approximate fitness functions

  • Y Jin
  • IEEE Transactions on Evolutionary Computation
  • 2002
1 Excerpt

Similar Papers

Loading similar papers…