Bayesian inference in estimation of distribution algorithms
Gallagher, Marcus, Wood, Ian A., Keith, Jonathan M., & Sofronov, George (2007) Bayesian inference in estimation of distribution algorithms. In IEEE Congress on Evolutionary Computation, 2007 (CEC 2007), 25-28 September 2007, Singapore.
Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAG c.
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|Item Type:||Conference Paper|
|Keywords:||Bayes methods, estimation theory, optimisation statistical distributions|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||06 Aug 2008 00:00|
|Last Modified:||29 Feb 2012 13:40|
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