Bayesian inference in estimation of distribution algorithms

Marcus Gallagher, Ian Wood, Jonathan Keith, George Sofronov

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

14 Citations (Scopus)


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, UMDA(c)(G).
Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Congress on Evolutionary Computation
EditorsArthur Tay
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages127 - 133
Number of pages7
ISBN (Print)9781424413393
Publication statusPublished - 2007
Externally publishedYes
EventIEEE Congress on Evolutionary Computation 2007 - Singapore, Singapore
Duration: 25 Sept 200728 Sept 2007 (Proceedings)


ConferenceIEEE Congress on Evolutionary Computation 2007
Abbreviated titleIEEE CEC 2007
Internet address

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