iBOA: The Incremental Bayesian Optimization Algorithm

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This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Also available at the MEDAL web site, http://medal.cs.umsl.edu/

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Neural and Evolutionary Computing, Artificial Intelligence, I.2.6; I.2.8; G.1.6

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http://salesiana.dossiersoluciones.com/handle/123456789/165935

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0801.3113
 http://arxiv.org/abs/0801.3113
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Press, 455-462

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arXiv