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Evolutionary many-objective optimization using preference on hyperplane

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 91 - 92

https://doi.org/10.1145/2598394.2598420

Published: 12 July 2014 Publication History

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Abstract

This paper proposes to represent the preference of a decision maker by Gaussian functions on a hyperplane. The preference is used to evaluate non-dominated solutions as a second criterion instead of the crowding distance in NSGA-II. High performance of our proposal is demonstrated for many-objective DTLZ problems.

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Cited By

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Ding RDong HHe JLi T(2019)A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference pointsApplied Soft Computing10.1016/j.asoc.2019.02.040Online publication date: Mar-2019
https://doi.org/10.1016/j.asoc.2019.02.040
Shu ZWang W(2018)Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective OptimizationIEEE Access10.1109/ACCESS.2018.28762736(68701-68715)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2876273
Xin BChen LChen JIshibuchi HHirota KLiu B(2018)Interactive Multiobjective Optimization: A Review of the State-of-the-ArtIEEE Access10.1109/ACCESS.2018.28568326(41256-41279)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2856832
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Index Terms

Evolutionary many-objective optimization using preference on hyperplane
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information & Contributors

Information

Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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Poster

Conference

GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Cited By

View all

Ding RDong HHe JLi T(2019)A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference pointsApplied Soft Computing10.1016/j.asoc.2019.02.040Online publication date: Mar-2019
https://doi.org/10.1016/j.asoc.2019.02.040
Shu ZWang W(2018)Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective OptimizationIEEE Access10.1109/ACCESS.2018.28762736(68701-68715)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2876273
Xin BChen LChen JIshibuchi HHirota KLiu B(2018)Interactive Multiobjective Optimization: A Review of the State-of-the-ArtIEEE Access10.1109/ACCESS.2018.28568326(41256-41279)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2856832
Wang CWang YWang KDong YYang Y(2017)An Improved Hybrid Algorithm Based on Biogeography/Complex and Metropolis for Many‐Objective OptimizationMathematical Problems in Engineering10.1155/2017/24628912017:1Online publication date: 30-Mar-2017
https://doi.org/10.1155/2017/2462891
Narukawa KSetoguchi YTanigaki YOlhofer MSendhoff BIshibuchi H(2016)Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1674-920:7(2733-2757)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00500-015-1674-9
Setoguchi YNarukawa KIshibuchi H(2015)A Knee-Based EMO Algorithm with an Efficient Method to Update Mobile Reference PointsEvolutionary Multi-Criterion Optimization10.1007/978-3-319-15934-8_14(202-217)Online publication date: 18-Mar-2015
https://doi.org/10.1007/978-3-319-15934-8_14
Tanigaki YNarukawa KNojima YIshibuch H(2014)Preference-based NSGA-II for many-objective knapsack problems2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)10.1109/SCIS-ISIS.2014.7044821(637-642)Online publication date: Dec-2014
https://doi.org/10.1109/SCIS-ISIS.2014.7044821

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