IDA - Intelligent Data Analysis Research Group

BibTeX Entry

@inproceedings{PosikMENDEL2007OvC,
  file = {OvC.pdf},
  category = {ida-nit-publications},
  author = {Petr Po{\v s}{\'i}k},
  title = {Optimization via Classification},
  booktitle = {MENDEL 2007 - 13th International Conference on Soft Computing},
  editor = {Radomil Matou{\v s}ek},
  volume = {1},
  year = {2007},
  organization = {Brno University of Technology},
  address = {Brno, Czech Republic},
  pages = {12--17},
  keywords = {optimization elliptic classifier Gauss distribution},
  note = {ISBN 978-80214-3473-8},
  abstract = {The vast majority of population-based optimization algorithms use selection in such a way that the non-selected individuals do not have any effect on the evolution at all, even though they may carry a valueable information --- information about the search space areas where the search should be suppressed and/or about the local shape of the search distribution. This article describes a unified way of taking advantage of the information hidden in the non-selected individuals in the framework of evolutionary algorithms: first, build a classifier discriminating between selected and non-selected individuals, then turn the description of selected individuals into a search distribution, and sample new offspring from it. The concept is verified by a simple real-valued evolutionary algorithm which outperforms the state-of-the-art evolutionary strategy with covariance matrix adaptation (CMA-ES) on selected test functions in all tested search space dimensionalities. Finally, the article proposes some guidelines for future work to make this algorithm generally applicable.},
  vvvs = {1},
  obory = {JC, JD},
  zamer = {BIO},
  projnum = {MSM6840770012 (VZ L. Lhotsk{\'e})},
}


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