Recombinative EMCMC algorithms

M.M. Drugan; D. Thierens

doi:10.1109/CEC.2005.1554944

Recombinative EMCMC algorithms

M.M. Drugan
, D. Thierens

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

3 Citaten (Scopus)

2 Downloads (Pure)

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Evolutionary Markov Chain Monte Carlo (EMCMC) is a class of algorithms obtained by merging Markov chain Monte Carlo algorithms with evolutionary computation methods. EMCMC integrates techniques from the EC framework (population, recombination and selection) into the MCMC framework to increase the performance of the standard MCMC algorithms. In this paper, we show how to use recombination operators in EMCMC and how to combine them with other existing MCMC techniques (e.g. mutation and selection). We illustrate these principles by means of an example.

Originele taal-2	Engels
Titel	The 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	2024-2031
Aantal pagina's	8
ISBN van geprinte versie	0780393635
DOI's	https://doi.org/10.1109/CEC.2005.1554944
Status	Gepubliceerd - 2005
Extern gepubliceerd	Ja
Evenement	2005 IEEE Congress on Evolutionary Computation, CEC 2005 - Edinburgh, Scotland, Verenigd Koninkrijk Duur: 2 sep. 2005 → 5 sep. 2005

Congres

Congres	2005 IEEE Congress on Evolutionary Computation, CEC 2005
Land/Regio	Verenigd Koninkrijk
Stad	Edinburgh, Scotland
Periode	2/09/05 → 5/09/05
Ander	The 2005 IEE Congress on Evolutionary Computation (CEC 2005)

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title = "Recombinative EMCMC algorithms",

abstract = "Evolutionary Markov Chain Monte Carlo (EMCMC) is a class of algorithms obtained by merging Markov chain Monte Carlo algorithms with evolutionary computation methods. EMCMC integrates techniques from the EC framework (population, recombination and selection) into the MCMC framework to increase the performance of the standard MCMC algorithms. In this paper, we show how to use recombination operators in EMCMC and how to combine them with other existing MCMC techniques (e.g. mutation and selection). We illustrate these principles by means of an example. ",

author = "M.M. Drugan and D. Thierens",

year = "2005",

doi = "10.1109/CEC.2005.1554944",

language = "English",

isbn = "0780393635",

pages = "2024--2031",

booktitle = "The 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2005 IEEE Congress on Evolutionary Computation, CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

}

Drugan, MM & Thierens, D 2005, Recombinative EMCMC algorithms. in The 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland. Institute of Electrical and Electronics Engineers, Piscataway, blz. 2024-2031, 2005 IEEE Congress on Evolutionary Computation, CEC 2005, Edinburgh, Scotland, Verenigd Koninkrijk, 2/09/05. https://doi.org/10.1109/CEC.2005.1554944

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AU - Drugan, M.M.

AU - Thierens, D.

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N2 - Evolutionary Markov Chain Monte Carlo (EMCMC) is a class of algorithms obtained by merging Markov chain Monte Carlo algorithms with evolutionary computation methods. EMCMC integrates techniques from the EC framework (population, recombination and selection) into the MCMC framework to increase the performance of the standard MCMC algorithms. In this paper, we show how to use recombination operators in EMCMC and how to combine them with other existing MCMC techniques (e.g. mutation and selection). We illustrate these principles by means of an example.

AB - Evolutionary Markov Chain Monte Carlo (EMCMC) is a class of algorithms obtained by merging Markov chain Monte Carlo algorithms with evolutionary computation methods. EMCMC integrates techniques from the EC framework (population, recombination and selection) into the MCMC framework to increase the performance of the standard MCMC algorithms. In this paper, we show how to use recombination operators in EMCMC and how to combine them with other existing MCMC techniques (e.g. mutation and selection). We illustrate these principles by means of an example.

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U2 - 10.1109/CEC.2005.1554944

DO - 10.1109/CEC.2005.1554944

M3 - Conference contribution

AN - SCOPUS:27144554009

SN - 0780393635

SP - 2024

EP - 2031

BT - The 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2005 IEEE Congress on Evolutionary Computation, CEC 2005

Y2 - 2 September 2005 through 5 September 2005

ER -

Recombinative EMCMC algorithms

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