A review on probabilistic graphical models in evolutionary computation

  title={A review on probabilistic graphical models in evolutionary computation},
  author={Pedro Larra{\~n}aga and Hossein Karshenas and Concha Bielza and Roberto Santana},
  journal={J. Heuristics},
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for… CONTINUE READING
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Optimization in continuous domains by learning and simulation of Gaussian networks. In: Conference on Genetic and Evolutionary Computation

  • P. Larranaga, R. Etxeberria, J. Lozano, J. Pena
  • Workshop Program,
  • 2000
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