IDA - Intelligent Data Analysis Research Group

BibTeX Entry

@inproceedings{PosikDHMS2008SimpleGaussianEDA,
  file = {Posik-DHMS2008.pdf},
  category = {ida-publications},
  author = {Petr Po{\v s}{\'i}k},
  title = {Gaussian EDA and Truncation Selection: Setting Limits for Sustainable Progress},
  booktitle = {IEEE SMC International Conference on Distributed Human-Machine Systems (DHMS 2008)},
  editor = {Vladim{\'i}r Ma{\v r}{\'i}k et al.},
  volume = {1},
  year = {2008},
  organization = {IEEE SMC Society},
  pages = {240--245},
  keywords = {evolutionary computation, estimation of distribution algorithms, theoretical model},
  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 to enlarge the maximum likelihood estimate of standard deviation $\sigma$ by a constant factor $k$ each generation. This paper surveys and broadens the theoretical models of the behaviour of this simple EDA on 1D problems and derives the limits for the constant $k$. The behaviour of this simple EDA with various values of $k$ is analysed and the agreement of the model with the reality is confirmed.},
  vvvs = {1},
  obory = {JC, JD},
  projnum = {13/08008/13133},
  projects = {MLSC},
}


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