research-article

G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets

Authors:

Giovanni Lizarraga-Lizarraga,

Arturo Hernandez-Aguirre,

Salvador Botello-RiondaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 665 - 672

https://doi.org/10.1145/1389095.1389227

Published: 12 July 2008 Publication History

Abstract

An open problem in multiobjective optimization using the Pareto optimality criteria, is how to evaluate the performance of different evolutionary algorithms that solve multi-objective problems. As the output of these algorithms is a non-dominated set (NS), this problem can be reduced to evaluate what NS is better than the others based on their projection on the objective space. In this work we propose a new performance measure for the evaluation of non-dominated sets, that ranks a set of NSs based on their convergence and dispersion. Its evaluations of the NSs agree with intuition. Also, we introduce a benchmark of test cases to evaluate performance measures, that considers several topologies of the Pareto Front.

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Cited By

Jameel MAbouhawwash M(2024)A new proximity metric based on optimality conditions for single and multi-objective optimization: Method and validationExpert Systems with Applications10.1016/j.eswa.2023.122677241(122677)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122677
Pan LZhang YZhou YHuttunen HBhattacharyya S(2023)Multi-Objective Design Optimization for Image Classification Using Elastic Neural Networks2023 57th Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS56502.2023.10089753(1-6)Online publication date: 22-Mar-2023
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Di Maio FMarchetti SZio E(2023)Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and managementReliability Engineering & System Safety10.1016/j.ress.2023.109245235(109245)Online publication date: Jul-2023
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Index Terms

G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets
1. Computing methodologies
  1. Artificial intelligence

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Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Jameel MAbouhawwash M(2024)A new proximity metric based on optimality conditions for single and multi-objective optimization: Method and validationExpert Systems with Applications10.1016/j.eswa.2023.122677241(122677)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122677
Pan LZhang YZhou YHuttunen HBhattacharyya S(2023)Multi-Objective Design Optimization for Image Classification Using Elastic Neural Networks2023 57th Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS56502.2023.10089753(1-6)Online publication date: 22-Mar-2023
https://doi.org/10.1109/CISS56502.2023.10089753
Di Maio FMarchetti SZio E(2023)Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and managementReliability Engineering & System Safety10.1016/j.ress.2023.109245235(109245)Online publication date: Jul-2023
https://doi.org/10.1016/j.ress.2023.109245
Wüthrich DZeverino MBourhis JBochud FMoeckli R(2023)Influence of optimisation parameters on directly deliverable Pareto fronts explored for prostate cancerPhysica Medica10.1016/j.ejmp.2023.103139114(103139)Online publication date: Oct-2023
https://doi.org/10.1016/j.ejmp.2023.103139
Jensen PZhang JWu Q(2020)Technical note: Interpolated Pareto surface similarity metrics for multi‐criteria optimization in radiation therapyMedical Physics10.1002/mp.1454147:12(6450-6457)Online publication date: 9-Nov-2020
https://doi.org/10.1002/mp.14541
Li MYao X(2019)Quality Evaluation of Solution Sets in Multiobjective OptimisationACM Computing Surveys10.1145/330014852:2(1-38)Online publication date: 18-Mar-2019
https://dl.acm.org/doi/10.1145/3300148
Li MYang SLiu XEsparcia-Alcázar A(2015)A Performance Comparison Indicator for Pareto Front Approximations in Many-Objective OptimizationProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754687(703-710)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754687
Chand SWagner M(2015)Evolutionary many-objective optimization: A quick-start guideSurveys in Operations Research and Management Science10.1016/j.sorms.2015.08.00120:2(35-42)Online publication date: Dec-2015
https://doi.org/10.1016/j.sorms.2015.08.001
Miqing Li Shengxiang Yang Xiaohui Liu (2014)Diversity Comparison of Pareto Front Approximations in Many-Objective OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2014.231065144:12(2568-2584)Online publication date: Dec-2014
https://doi.org/10.1109/TCYB.2014.2310651
Bringmann KFriedrich T(2013)Approximation quality of the hypervolume indicatorArtificial Intelligence10.1016/j.artint.2012.09.005195(265-290)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.artint.2012.09.005
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