Actas de congresos
Online Learning In Estimation Of Distribution Algorithms For Dynamic Environments
Registro en:
9781424478347
2011 Ieee Congress Of Evolutionary Computation, Cec 2011. , v. , n. , p. 62 - 69, 2011.
10.1109/CEC.2011.5949598
2-s2.0-80051999168
Autor
Goncalves A.R.
Von Zuben F.J.
Institución
Resumen
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. © 2011 IEEE.
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