A quality metric for multi-objective optimization based on hierarchical clustering techniques

Frederico G. Guimarães; Elizabeth F. Wanner; Ricardo H.C. Takahashi

doi:10.1109/CEC.2009.4983362

A quality metric for multi-objective optimization based on hierarchical clustering techniques

Frederico G. Guimarães, Elizabeth F. Wanner, Ricardo H.C. Takahashi

Research output: Chapter in Book/Published conference output › Conference publication

10 Citations (Scopus)

Abstract

This paper presents the Hierarchical Cluster Counting (HCC), a new quality metric for nondominated sets generated by multi-objective optimizers that is based on hierarchical clustering techniques. In the computation of the HCC, the samples in the estimate set are sequentially grouped into clusters. The nearest clusters in a given iteration are joined together until all the data is grouped in only one class. The distances of fusion used at each iteration of the hierarchical agglomerative clustering process are integrated into one value, which is the value of the HCC for that estimate set. The examples show that the HCC metric is able to evaluate both the extension and uniformity of the samples in the estimate set, making it suitable as a unary diversity metric for multiobjective optimization.

Original language	English
Title of host publication	2009 IEEE Congress on Evolutionary Computation
Publisher	IEEE
Pages	3292-3299
Number of pages	8
ISBN (Print)	9781424429592
DOIs	https://doi.org/10.1109/CEC.2009.4983362
Publication status	Published - 2009
Event	2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway Duration: 18 May 2009 → 21 May 2009

Conference

Conference	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Country/Territory	Norway
City	Trondheim
Period	18/05/09 → 21/05/09

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10.1109/CEC.2009.4983362

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title = "A quality metric for multi-objective optimization based on hierarchical clustering techniques",

abstract = "This paper presents the Hierarchical Cluster Counting (HCC), a new quality metric for nondominated sets generated by multi-objective optimizers that is based on hierarchical clustering techniques. In the computation of the HCC, the samples in the estimate set are sequentially grouped into clusters. The nearest clusters in a given iteration are joined together until all the data is grouped in only one class. The distances of fusion used at each iteration of the hierarchical agglomerative clustering process are integrated into one value, which is the value of the HCC for that estimate set. The examples show that the HCC metric is able to evaluate both the extension and uniformity of the samples in the estimate set, making it suitable as a unary diversity metric for multiobjective optimization.",

author = "Guimar{\~a}es, {Frederico G.} and Wanner, {Elizabeth F.} and Takahashi, {Ricardo H.C.}",

year = "2009",

doi = "10.1109/CEC.2009.4983362",

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pages = "3292--3299",

booktitle = "2009 IEEE Congress on Evolutionary Computation",

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note = "2009 IEEE Congress on Evolutionary Computation, CEC 2009 ; Conference date: 18-05-2009 Through 21-05-2009",

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AU - Guimarães, Frederico G.

AU - Wanner, Elizabeth F.

AU - Takahashi, Ricardo H.C.

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N2 - This paper presents the Hierarchical Cluster Counting (HCC), a new quality metric for nondominated sets generated by multi-objective optimizers that is based on hierarchical clustering techniques. In the computation of the HCC, the samples in the estimate set are sequentially grouped into clusters. The nearest clusters in a given iteration are joined together until all the data is grouped in only one class. The distances of fusion used at each iteration of the hierarchical agglomerative clustering process are integrated into one value, which is the value of the HCC for that estimate set. The examples show that the HCC metric is able to evaluate both the extension and uniformity of the samples in the estimate set, making it suitable as a unary diversity metric for multiobjective optimization.

AB - This paper presents the Hierarchical Cluster Counting (HCC), a new quality metric for nondominated sets generated by multi-objective optimizers that is based on hierarchical clustering techniques. In the computation of the HCC, the samples in the estimate set are sequentially grouped into clusters. The nearest clusters in a given iteration are joined together until all the data is grouped in only one class. The distances of fusion used at each iteration of the hierarchical agglomerative clustering process are integrated into one value, which is the value of the HCC for that estimate set. The examples show that the HCC metric is able to evaluate both the extension and uniformity of the samples in the estimate set, making it suitable as a unary diversity metric for multiobjective optimization.

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PB - IEEE

T2 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

Y2 - 18 May 2009 through 21 May 2009

ER -

A quality metric for multi-objective optimization based on hierarchical clustering techniques

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