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

Access to Document

10.1109/CEC.2005.1554944

Link to publication in Scopus

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Cite this

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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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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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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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