IDA - Intelligent Data Analysis Research Group

BibTeX Entry

  file = {Posik2010SLSQuestionsToBeAnswered.pdf},
  category = {ida-publications},
  author = {Petr Po{\v s}{\'i}k},
  title = {Stochastic local search in continuous domains: questions to be answered when designing a novel algorithm},
  booktitle = {GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation},
  year = {2010},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {1937-1944},
  url = {},
  keywords = {continuous, ea, eda, feature, survey},
  abstract = {Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains are surveyed in this article. The similarities and differences among them are emphasized and it is shown that they all can be described in a common framework of stochastic local search -- a class of methods previously defined mainly for combinatorial problems. Based on the lessons learned from the surveyed algorithms, a set of algorithm features (or, questions to be answered) is extracted. An algorithm designer can take advantage of these features and by deciding on each of them, she can construct a novel algorithm. A few examples in this direction are shown.},
  vvvs = {1},
  obory = {JC,JD},
  projects = {MLSC},

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