Population optimization algorithm based on ICA

Qingfu Zhang; N. M. Allinson; Hujun Yin

doi:10.1109/ECNN.2000.886216

Population optimization algorithm based on ICA

Qingfu Zhang, N. M. Allinson, Hujun Yin

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

We propose a new population optimization algorithm called univariate marginal distribution algorithm with independent component analysis (UMDA/ICA). Our main idea is to incorporate ICA into the UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

Original language	English
Title of host publication	Proceedings of the 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks
Publisher	IEEE
Pages	33-36
ISBN (Print)	0780365720, 9780780365728
DOIs	https://doi.org/10.1109/ECNN.2000.886216
Publication status	Published - 2000
Externally published	Yes
Event	1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN 2000 - San Antonio, United States Duration: 11 May 2000 → 13 May 2000

Conference

Conference	1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN 2000
Place	United States
City	San Antonio
Period	11/05/00 → 13/05/00

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10.1109/ECNN.2000.886216

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@inproceedings{de6015a31ba84f47bcb72e26ab02e0f5,

title = "Population optimization algorithm based on ICA",

abstract = "We propose a new population optimization algorithm called univariate marginal distribution algorithm with independent component analysis (UMDA/ICA). Our main idea is to incorporate ICA into the UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.",

author = "Qingfu Zhang and Allinson, {N. M.} and Hujun Yin",

year = "2000",

doi = "10.1109/ECNN.2000.886216",

language = "English",

isbn = "0780365720",

pages = "33--36",

booktitle = "Proceedings of the 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks",

publisher = "IEEE",

address = "United States",

note = "1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN 2000 ; Conference date: 11-05-2000 Through 13-05-2000",

}

Zhang, Q, Allinson, NM & Yin, H 2000, Population optimization algorithm based on ICA. in Proceedings of the 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks., 886216, IEEE, pp. 33-36, 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN 2000, San Antonio, United States, 11/05/00. https://doi.org/10.1109/ECNN.2000.886216

Population optimization algorithm based on ICA. / Zhang, Qingfu; Allinson, N. M.; Yin, Hujun.
Proceedings of the 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. IEEE, 2000. p. 33-36 886216.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Population optimization algorithm based on ICA

AU - Zhang, Qingfu

AU - Allinson, N. M.

AU - Yin, Hujun

PY - 2000

Y1 - 2000

N2 - We propose a new population optimization algorithm called univariate marginal distribution algorithm with independent component analysis (UMDA/ICA). Our main idea is to incorporate ICA into the UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

AB - We propose a new population optimization algorithm called univariate marginal distribution algorithm with independent component analysis (UMDA/ICA). Our main idea is to incorporate ICA into the UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84952782341&origin=recordpage

U2 - 10.1109/ECNN.2000.886216

DO - 10.1109/ECNN.2000.886216

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780365720

SN - 9780780365728

SP - 33

EP - 36

BT - Proceedings of the 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks

PB - IEEE

T2 - 1st IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN 2000

Y2 - 11 May 2000 through 13 May 2000

ER -

Population optimization algorithm based on ICA

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