Recombinative EMCMC algorithms

M.M. Drugan; D. Thierens

doi:10.1109/CEC.2005.1554944

Recombinative EMCMC algorithms

M.M. Drugan, D. Thierens

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

3 Citations (Scopus)

2 Downloads (Pure)

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.

Original language	English
Title of host publication	The 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	2024-2031
Number of pages	8
ISBN (Print)	0780393635
DOIs	https://doi.org/10.1109/CEC.2005.1554944
Publication status	Published - 2005
Externally published	Yes
Event	2005 IEEE Congress on Evolutionary Computation, CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sep 2005 → 5 Sep 2005

Conference

Conference	2005 IEEE Congress on Evolutionary Computation, CEC 2005
Country/Territory	United Kingdom
City	Edinburgh, Scotland
Period	2/09/05 → 5/09/05
Other	The 2005 IEE Congress on Evolutionary Computation (CEC 2005)

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10.1109/CEC.2005.1554944

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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. ",

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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",

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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, pp. 2024-2031, 2005 IEEE Congress on Evolutionary Computation, CEC 2005, Edinburgh, Scotland, United Kingdom, 2/09/05. https://doi.org/10.1109/CEC.2005.1554944

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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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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

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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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