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Title: Bayesian inference in estimation of distribution algorithms
Related: Congress on Evolutionary Computation (25 - 28 September 2007 : Singapore)
Related: Evolutionary Computation, 2007, CEC 2007, IEEE Congress on : date 25-28 Sept. 2007, p.127-133
DOI: 10.1109/CEC.2007.4424463
Publisher: Piscataway, N.J : IEEE
Date: 2007
Author/Creator: Gallagher, Marcus
Author/Creator: Wood, Ian
Author/Creator: Keith, Jonathan
Author/Creator: Sofronov, George
Description: 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, UMDAcG.
Description: 7 page(s)
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Statistics

Identifier: http://hdl.handle.net/1959.14/139083
Identifier: ISBN:9781424413409
Identifier: mq_res-ext-2-s2.0-77952750332
Language: eng
Rights: Copyright 2007 IEEE. Reprinted from Evolutionary Computation, 2007, CEC 2007, IEEE Congress on : date 25-28 Sept. 2007. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or 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 must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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