research-article

Objective reduction using a feature selection technique

Authors:

Antonio López Jaimes,

Carlos A. Coello Coello,

Debrup ChakrabortyAuthors Info & Claims

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

Pages 673 - 680

https://doi.org/10.1145/1389095.1389228

Published: 12 July 2008 Publication History

Abstract

This paper introduces two new algorithms to reduce the number of objectives in a multiobjective problem by identifying the most conflicting objectives. The proposed algorithms are based on a feature selection technique proposed by Mitra et. al. [11]. One algorithm is intended to determine the minimum subset of objectives that yields the minimum error possible, while the other finds a subset of objectives of a given size that yields the minimum error. To validate these algorithms we compare their results against those obtained by two similar algorithms recently proposed. The comparative study shows that our algorithms are very competitive with respect to the reference algorithms. Additionally, our approaches require a lower computational time. Also, in this study we propose to use the inverted generational distance to evaluate the quality of a subset of objectives.

References

[1]

P. J. Bentley and J. P. Wakefield. Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms. In P. K. Chawdhry, R. Roy, and R. K. Pant, editors, Soft Computing in Engineering Design and Manufacturing, Part 5, pages 231--240, London, June 1997. Springer Verlag London Limited. (Presented at the 2nd On-line World Conference on Soft Computing in Design and Manufacturing (WSC2)).]]

[2]

D. Brockhoff and E. Zitzler. Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary Multiobjective Optimization. In Parallel Problem Solving from Nature IX, pages 533--542. Springer-Verlag, 2006.]]

Digital Library

[3]

D. Brockhoff and E. Zitzler. Offline and Online Objective Reduction in Evolutionary Multiobjective Optimization Based on Objective Conflicts. TIK Report 269, Institut für Technische Informatik und Kommunikationsnetze, ETH Zürich, Apr. 2007.]]

[4]

C. A. Coello Coello, G. B. Lamont, and D. A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, second edition, September 2007. ISBN 978-0-387-33254-3.]]

Digital Library

[5]

K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature VI, pages 849--858, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.]]

Digital Library

[6]

K. Deb and D. K. Saxena. Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In 2006 IEEE Congress on Evolutionary Computation (CEC'2006), pages 3353--3360, Vancouver, BC, Canada, July 2006. IEEE.]]

[7]

F. di Pierro. Many-Objective Evolutionary Algorithms and Applications to Water Resources Engineering. PhD thesis, School of Engineering, Computer Science and Mathematics, UK, August 2006.]]

[8]

M. Farina and P. Amato. On the Optimal Solution Definition for Many-criteria Optimization Problems. In Proceedings of the NAFIPS-FLINT International Conference'2002, pages 233--238, Piscataway, New Jersey, June 2002. IEEE Service Center.]]

[9]

E. J. Hughes. Evolutionary Many-Objective Optimisation: Many Once or One Many? In 2005 IEEE Congress on Evolutionary Computation (CEC'2005), volume 1, pages 222--227, Edinburgh, Scotland, September 2005. IEEE Service Center.]]

[10]

V. Khare. Performance Scaling of Multi-Objective Evolutionary Algorithms. Master's thesis, School of Computer Science, The University of Birmingham, Edgbaston, Birmingan, UK, September 2002.]]

[11]

P. Mitra, C. Murthy, and S. Pal. Unsupervised feature selection using feature similarity. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(3):301--312, 2002.]]

Digital Library

[12]

A. Sülflow, N. Drechsler, and R. Drechsler. Robust Multi-objective Optimization in High Dimensional Spaces. In S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, and T. Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 715--726, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.]]

Digital Library

[13]

D. A. V. Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.]]

Digital Library

[14]

T. Wagner, N. Beume, and B. Naujoks. Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization. In S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, and T. Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 742--756, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.]]

Digital Library

[15]

E. Zitzler and L. Thiele. Multiobjective Optimization Using Evolutionary Algorithms-A Comparative Study. In A. E. Eiben, editor, Parallel Problem Solving from Nature V, pages 292--301, Amsterdam, September 1998. Springer-Verlag.]]

Digital Library

Cited By

Wang MHeidari AChen LWang RLiu MShao LChen H(2025)Adaptive density-based clustering for many objective similarity or redundancy evolutionary optimizationExpert Systems with Applications10.1016/j.eswa.2024.126060266(126060)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.126060
Li GWang ZSun JZhang Q(2024)Objective Extraction for Simplifying Many-Objective Solution SetsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33014018:1(337-349)Online publication date: Feb-2024
https://doi.org/10.1109/TETCI.2023.3301401
Voelker SAckermann PGranderath MKortmann CViell JMitsos Avon der Assen N(2024)Identifying key environmental objectives for integrated process and fuel designSustainable Energy & Fuels10.1039/D3SE01602A8:9(1966-1982)Online publication date: 2024
https://doi.org/10.1039/D3SE01602A
Show More Cited By

Index Terms

Objective reduction using a feature selection technique
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

Recommendations

Objective reduction for many-objective optimization problems using objective subspace extraction

Multi-objective evolutionary algorithms (MOEAs) have shown their effectiveness in exploring a well converged and diversified approximation set for multi-objective optimization problems (MOPs) with 2 and 3 objectives. However, most of them perform poorly ...
Objective reduction in evolutionary multiobjective optimization: Theory and applications

Many-objective problems represent a major challenge in the field of evolutionary multiobjective optimization---in terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in ...
Multitask Feature Selection for Objective Reduction
Evolutionary Multi-Criterion Optimization
Abstract
Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated ...

Comments

Information & Contributors

Information

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

73
Total Citations
View Citations
516
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang MHeidari AChen LWang RLiu MShao LChen H(2025)Adaptive density-based clustering for many objective similarity or redundancy evolutionary optimizationExpert Systems with Applications10.1016/j.eswa.2024.126060266(126060)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.126060
Li GWang ZSun JZhang Q(2024)Objective Extraction for Simplifying Many-Objective Solution SetsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33014018:1(337-349)Online publication date: Feb-2024
https://doi.org/10.1109/TETCI.2023.3301401
Voelker SAckermann PGranderath MKortmann CViell JMitsos Avon der Assen N(2024)Identifying key environmental objectives for integrated process and fuel designSustainable Energy & Fuels10.1039/D3SE01602A8:9(1966-1982)Online publication date: 2024
https://doi.org/10.1039/D3SE01602A
Xu HYuan Y(2024)Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysisIntelligent Evolutionary Optimization10.1016/B978-0-443-27400-8.00004-6(107-153)Online publication date: 2024
https://doi.org/10.1016/B978-0-443-27400-8.00004-6
Gaspar-Cunha ACosta PMonaco FDelbem A(2023)Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization ProblemsEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_7(86-100)Online publication date: 9-Mar-2023
https://doi.org/10.1007/978-3-031-27250-9_7
Shen JWang PWang X(2022)A Controlled Strengthened Dominance Relation for Evolutionary Many-Objective OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2020.301599852:5(3645-3657)Online publication date: May-2022
https://doi.org/10.1109/TCYB.2020.3015998
Guo X(2022)A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization ProblemsIEEE Access10.1109/ACCESS.2022.318876210(72825-72838)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3188762
Gupta RNanda S(2022)Objective reduction in many-objective optimization with social spider algorithm for cloud detection in satellite imagesSoft Computing10.1007/s00500-021-06655-8Online publication date: 10-Jan-2022
https://doi.org/10.1007/s00500-021-06655-8
Yang LHu XLi K(2021)A vector angles-based many-objective particle swarm optimization algorithm using archiveApplied Soft Computing10.1016/j.asoc.2021.107299106:COnline publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107299
Cuate OSchütze O(2020)Pareto Explorer for Finding the Knee for Many Objective Optimization ProblemsMathematics10.3390/math81016518:10(1651)Online publication date: 24-Sep-2020
https://doi.org/10.3390/math8101651
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten