Online learning in estimation of distribution algorithms for dynamic environments   [OL] [DA] [DE]

by

Goncalves, A., R. and Zuben, F., J., V.

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Info: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (Conference proceedings), 2011, p. 62-69
Keywords:Estimation of distribution algorithms, Dynamic, uncertain environments.
Abstract:
In this paper, we propose an estimation of distribution algorithm based on an inexpensive Gaussian mixture model [GMM] with online learning, [OL] which will be employed in dynamic optimization. Here, the mixture model stores a vector of sufficient statistics [SS] of the best solutions, which is subsequently used to obtain the parameters of the Gaussian components. This approach is able to incorporate into the current mixture model potentially relevant information of the previous and current iterations. The online nature of the proposal is desirable in the context of dynamic optimization, where prompt reaction to new scenarios should be promoted. To analyze the performance of our proposal, a set of dynamic optimization problems [OP] in continuous domains was considered with distinct levels of complexity, and the obtained results were compared to the results produced by other existing algorithms in the dynamic optimization literature.
Notes:
CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
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BibTex:
@InProceedings{Goncalves:2011:Olieodafde,
  title     = {Online learning in estimation of distribution algorithms for dynamic environments},
  author    = {Andre R. Goncalves and Fernando J. {Von Zuben}},
  pages     = {62--69},
  booktitle = {Proceedings of the 2011 IEEE Congress on Evolutionary Computation},
  year      = {2011},
  editor = "Alice E. Smith",
  month     = {5-8 June},
  address   = {New Orleans, USA},
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  ISBN      = {0-7803-8515-2},
  keywords  = {Estimation of distribution algorithms, Dynamic and uncertain environments.},
  abstract  = {
In this paper, we propose an estimation of distribution algorithm based on an
inexpensive Gaussian mixture model with online learning, which will be
employed in dynamic optimization. Here, the mixture model stores a vector of
sufficient statistics of the best solutions, which is subsequently used to
obtain the parameters of the Gaussian components. This approach is able to
incorporate into the current mixture model potentially relevant information of
the previous and current iterations. The online nature of the proposal is
desirable in the context of dynamic optimization, where prompt reaction to new
scenarios should be promoted. To analyze the performance of our proposal, a
set of dynamic optimization problems in continuous domains was considered with
distinct levels of complexity, and the obtained results were compared to the
results produced by other existing algorithms in the dynamic optimization
literature.
},
  notes =	{CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET. },
}


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