Bayesian inference in estimation of distribution algorithms

Marcus Gallagher; Ian Wood; Jonathan Keith; George Sofronov

doi:10.1109/CEC.2007.4424463

Bayesian inference in estimation of distribution algorithms

Marcus Gallagher, Ian Wood, Jonathan Keith, George Sofronov

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

14 Citations (Scopus)

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, UMDA(c)(G).

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Congress on Evolutionary Computation
Editors	Arthur Tay
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	127 - 133
Number of pages	7
Volume	1
ISBN (Print)	9781424413393
DOIs	https://doi.org/10.1109/CEC.2007.4424463
Publication status	Published - 2007
Externally published	Yes
Event	IEEE Congress on Evolutionary Computation 2007 - Singapore, Singapore Duration: 25 Sept 2007 → 28 Sept 2007 https://ieeexplore.ieee.org/xpl/conhome/4424445/proceeding (Proceedings)

Conference

Conference	IEEE Congress on Evolutionary Computation 2007
Abbreviated title	IEEE CEC 2007
Country/Territory	Singapore
City	Singapore
Period	25/09/07 → 28/09/07
Internet address	https://ieeexplore.ieee.org/xpl/conhome/4424445/proceeding (Proceedings)

Access to Document

10.1109/CEC.2007.4424463

Cite this

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title = "Bayesian inference in estimation of distribution algorithms",

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, UMDA(c)(G).",

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year = "2007",

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editor = "Arthur Tay",

booktitle = "Proceedings of the 2007 IEEE Congress on Evolutionary Computation",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE Congress on Evolutionary Computation 2007, IEEE CEC 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

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}

Gallagher, M, Wood, I, Keith, J & Sofronov, G 2007, Bayesian inference in estimation of distribution algorithms. in A Tay (ed.), Proceedings of the 2007 IEEE Congress on Evolutionary Computation. vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 127 - 133, IEEE Congress on Evolutionary Computation 2007, Singapore, Singapore, 25/09/07. https://doi.org/10.1109/CEC.2007.4424463

Bayesian inference in estimation of distribution algorithms. / Gallagher, Marcus; Wood, Ian; Keith, Jonathan et al.
Proceedings of the 2007 IEEE Congress on Evolutionary Computation. ed. / Arthur Tay. Vol. 1 USA: IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 127 - 133.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Wood, Ian

AU - Keith, Jonathan

AU - Sofronov, George

PY - 2007

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N2 - 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).

AB - 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).

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M3 - Conference Paper

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BT - Proceedings of the 2007 IEEE Congress on Evolutionary Computation

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ER -

Bayesian inference in estimation of distribution algorithms

Abstract

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