Empirically comparing the finite-time performance of simulation-optimization algorithms

@article{Dong2017EmpiricallyCT,
  title={Empirically comparing the finite-time performance of simulation-optimization algorithms},
  author={Naijia Anna Dong and David J. Eckman and Xueqi Zhao and Shane G. Henderson and Matthias Poloczek},
  journal={2017 Winter Simulation Conference (WSC)},
  year={2017},
  pages={2206-2217}
}
  • Naijia Anna Dong, David J. Eckman, +2 authors Matthias Poloczek
  • Published 2017
  • Mathematics, Computer Science
  • 2017 Winter Simulation Conference (WSC)
  • We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algorithms with strong finite-time performance. We investigate if the observed performance of the algorithms can be explained by properties of the problems, e.g., the number of decision variables, the topology of the objective function, or the magnitude of the simulation error. 
    Named Entity Recognition in Estonian
    11
    ConBO: Conditional Bayesian Optimization
    Dynamic origin-destination matrix calibration for large-scale network simulators
    8
    Scalable Global Optimization via Local Bayesian Optimization
    9
    Efficient simulation-based toll optimization for large-scale networks
    4

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 16 REFERENCES
    Hypothalamic and pituitary c-Jun N-terminal kinase 1 signaling coordinately regulates glucose metabolism
    144
    Cultivation of the Lansing Strain of Poliomyelitis Virus in Cultures of Various Human Embryonic Tissues.
    350
    Exploring Theories Identified in the Journal of Interior Design
    13
    A Simplex Method for Function Minimization
    19216
    A Testbed of Simulation-Optimization Problems
    56
    Implementation of the simultaneous perturbation algorithm for stochastic optimization
    578
    FiniteTimeSimOpt
    • 2017