Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/108056
Type: Artigo de evento
Title: Online Learning In Estimation Of Distribution Algorithms For Dynamic Environments
Author: Goncalves A.R.
Von Zuben F.J.
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. © 2011 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/CEC.2011.5949598
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-80051999168&partnerID=40&md5=2bc9f80df34551326323627d480f44f0
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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