IDA - Intelligent Data Analysis Research Group

BibTeX Entry

@inproceedings{PosikEvoNUM2008TrunSelGaussEDA,
  file = {Posik-EvoNUM2008-final.pdf},
  category = {ida-publications},
  author = {Petr Po{\v s}{\'i}k},
  title = {Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces},
  booktitle = {EvoWorkshops 2008},
  editor = {M. Giacobini et al.},
  series = {LNCS},
  volume = {4974},
  year = {2008},
  publisher = {Springer-Verlag Berlin Heidelberg},
  pages = {525-534},
  abstract = {In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of Gaussian distribution is enlarging the maximum likelihood estimate of the standard deviation by a constant factor k each generation. Such a factor should be large enough to prevent convergence on slopes of the fitness function, but should not be too large to allow the algorithm converge in the neighborhood of the optimum. Previous work showed that for truncation selection such admissible k exists in 1D case. In this article it is shown experimentaly, that for the Gaussian EDA with truncation selection in high-dimensional spaces no admissible k exists!},
  vvvs = {1},
  obory = {JC, JD},
  projnum = {13/08008/13133},
  projects = {MLSC},
}


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