Bayesian inference in estimation of distribution algorithms

Marcus Gallagher*, Ian Wood, Jonathan Keith, George Sofronov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

11 Citations (Scopus)
42 Downloads (Pure)

Abstract

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, UMDAc G.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Place of PublicationPiscataway, N.J
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages127-133
Number of pages7
ISBN (Print)1424413400, 9781424413409
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period25/09/0728/09/07

Bibliographical note

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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