To enhance continuous estimation of distribution algorithms by density ensembles

Yi HONG; He-Long LI; Sam KWONG; Qing-Sheng REN

doi:10.1109/ICMLC.2009.5212566

To enhance continuous estimation of distribution algorithms by density ensembles

Yi HONG, He-Long LI, Sam KWONG, Qing-Sheng REN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities are obtained by resampling operator and density estimation operator, and new candidate solutions are reproduced by sampling from all obtained densities. In subspaces continuous estimation of distribution algorithms, a population of densities are obtained by randomly selecting a subset of all variables and estimating the density of high quality solutions in this subspace. The above steps iterate and many densities of high quality solutions in different subspaces are achieved. New candidate solutions are reproduced through perturbing old promising solutions in these subspaces. © 2009 IEEE.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	95-100
Number of pages	6
ISBN (Print)	9781424437023
DOIs	https://doi.org/10.1109/ICMLC.2009.5212566
Publication status	Published - Jul 2009
Externally published	Yes
Event	2009 International Conference on Machine Learning and Cybernetics - Hebei, China Duration: 12 Jul 2009 → 15 Jul 2009

Conference

Conference	2009 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Hebei
Period	12/07/09 → 15/07/09

Keywords

Estimation of distribution algorithms
Optimization

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10.1109/ICMLC.2009.5212566

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title = "To enhance continuous estimation of distribution algorithms by density ensembles",

abstract = "This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities are obtained by resampling operator and density estimation operator, and new candidate solutions are reproduced by sampling from all obtained densities. In subspaces continuous estimation of distribution algorithms, a population of densities are obtained by randomly selecting a subset of all variables and estimating the density of high quality solutions in this subspace. The above steps iterate and many densities of high quality solutions in different subspaces are achieved. New candidate solutions are reproduced through perturbing old promising solutions in these subspaces. {\textcopyright} 2009 IEEE.",

keywords = "Estimation of distribution algorithms, Optimization",

author = "Yi HONG and He-Long LI and Sam KWONG and Qing-Sheng REN",

year = "2009",

month = jul,

doi = "10.1109/ICMLC.2009.5212566",

language = "English",

isbn = "9781424437023",

pages = "95--100",

booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

publisher = "IEEE",

note = "2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",

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T1 - To enhance continuous estimation of distribution algorithms by density ensembles

AU - HONG, Yi

AU - LI, He-Long

AU - KWONG, Sam

AU - REN, Qing-Sheng

PY - 2009/7

Y1 - 2009/7

N2 - This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities are obtained by resampling operator and density estimation operator, and new candidate solutions are reproduced by sampling from all obtained densities. In subspaces continuous estimation of distribution algorithms, a population of densities are obtained by randomly selecting a subset of all variables and estimating the density of high quality solutions in this subspace. The above steps iterate and many densities of high quality solutions in different subspaces are achieved. New candidate solutions are reproduced through perturbing old promising solutions in these subspaces. © 2009 IEEE.

AB - This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities are obtained by resampling operator and density estimation operator, and new candidate solutions are reproduced by sampling from all obtained densities. In subspaces continuous estimation of distribution algorithms, a population of densities are obtained by randomly selecting a subset of all variables and estimating the density of high quality solutions in this subspace. The above steps iterate and many densities of high quality solutions in different subspaces are achieved. New candidate solutions are reproduced through perturbing old promising solutions in these subspaces. © 2009 IEEE.

KW - Estimation of distribution algorithms

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=70350733980&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2009.5212566

DO - 10.1109/ICMLC.2009.5212566

M3 - Conference paper (refereed)

SN - 9781424437023

SP - 95

EP - 100

BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

PB - IEEE

T2 - 2009 International Conference on Machine Learning and Cybernetics

Y2 - 12 July 2009 through 15 July 2009

ER -

To enhance continuous estimation of distribution algorithms by density ensembles

Abstract

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Keywords

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