Over-selection : An attempt to boost EDA under small population size

Yi HONG; Sam KWONG; Qingsheng REN; Xiong WANG

doi:10.1109/CEC.2007.4424589

Over-selection : An attempt to boost EDA under small population size

Yi HONG, Sam KWONG, Qingsheng REN, Xiong WANG

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

7 Citations (Scopus)

Abstract

Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA. © 2007 IEEE.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Publisher	IEEE
Pages	1075-1182
ISBN (Print)	9781424413393
DOIs	https://doi.org/10.1109/CEC.2007.4424589
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Congress

Congress	2007 IEEE Congress on Evolutionary Computation
Abbreviated title	CEC 2007
Country/Territory	Singapore
Period	25/09/07 → 28/09/07

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10.1109/CEC.2007.4424589

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@inproceedings{5b80513ab53a43abaa32eec3407a7f00,

title = "Over-selection : An attempt to boost EDA under small population size",

abstract = "Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA. {\textcopyright} 2007 IEEE.",

author = "Yi HONG and Sam KWONG and Qingsheng REN and Xiong WANG",

year = "2007",

doi = "10.1109/CEC.2007.4424589",

language = "English",

isbn = "9781424413393",

pages = "1075--1182",

booktitle = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

publisher = "IEEE",

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

}

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T1 - Over-selection : An attempt to boost EDA under small population size

AU - HONG, Yi

AU - KWONG, Sam

AU - REN, Qingsheng

AU - WANG, Xiong

PY - 2007

Y1 - 2007

N2 - Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA. © 2007 IEEE.

AB - Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA. © 2007 IEEE.

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DO - 10.1109/CEC.2007.4424589

M3 - Conference paper (refereed)

SN - 9781424413393

SP - 1075

EP - 1182

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

PB - IEEE

T2 - 2007 IEEE Congress on Evolutionary Computation

Y2 - 25 September 2007 through 28 September 2007

ER -

Over-selection : An attempt to boost EDA under small population size

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