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Special Session/Workshop on Subset Selection in Evolutionary Multi-objective Optimization


Subset selection is an important research topic in the evolutionary multi-objective optimization (EMO) field. For example, in environmental selection of an EMO algorithm, the next population is selected from the merged population of the current and offspring populations. Subset selection is performed in each generation of an EMO algorithm. Subset selection can be also used as a post-processing procedure after the termination of the execution of an EMO algorithm. In this case, an external archive is maintained to store non-dominated solutions among the examined solutions. Subset selection is performed to select a pre-specified number of solutions from the external archive as the final output.


It is important to develop efficient subset selection methods so that a subset of solutions can be selected from the candidate solution set within reasonable and acceptable runtime. Especially when subset selection is used as a post-processing procedure, it is also important to clearly interpret the reason why the subset is selected. That is, to clearly explain the criteria of the subset selection methods. This special session/workshop aims to attract research works on subset selection in EMO.

The goal of this special session/workshop is to promote the development of the subset selection research in EMO. The development of the subset selection research will further promote the progress of the EMO field, including new EMO algorithm design, new decision-making methods, and bridging the gap of subset selection between the EMO and the machine learning fields.


This special session/workshop is intended to bring together researchers working in this and related areas to discuss all aspects of subset selection in EMO, including (but not limited to):
• Environmental selection in EMO algorithms
• Hypervolume-based subset selection
• IGD/IGD+ indicator-based subset selection
• Other performance indicator-based subset selection
• Distance-based subset selection
• Knee-based subset selection
• Exact, greedy, and evolutionary methods for subset selection in EMO
• Subset selection in the objective space
• Subset selection in the decision space
• Large-scale subset selection in EMO
• Subset selection for decision-making
• Theoretical and empirical studies on subset selection in EMO

Submission Guidelines

Please follow the submission guideline from the IEEE WCCI 2022 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Subset Selection in Evolutionary Multi-objective Optimization. All papers accepted and presented at IEEE WCCI 2022 will be included in the conference proceedings published by IEEE Xplore, which are typically indexed by EI.

Important Dates

Title and Abstract submission: January 31, 2022 (11:59 PM AoE). New submissions cannot be created past this deadline.
Complete Paper (pdf) submission: February 14, 2022 (11:59 PM AoE) STRICT DEADLINE
Notification of acceptance: April 26, 2022
Final paper submission: May 23, 2022


Ke Shang received the B.S. and Ph.D. degrees from Xi'an Jiaotong University, China, in 2009 and 2016, respectively. He is currently a research assistant professor at Southern University of Science and Technology, China. His current research interests include evolutionary multi-objective optimization and its applications. He received GECCO 2018, 2021 Best Paper Award and CEC 2019 First Runner-up Conference Paper Award, and best paper nominations at PPSN 2020.

Lie Meng Pang received her Bachelor of Engineering degree in Electronic and Telecommunication Engineering and Ph.D. degree in Electronic Engineering from the Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia, in 2012 and 2018, respectively. She is currently a research associate with the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), China. Her current research interests include evolutionary multi-objective optimization and fuzzy systems.

Hisao Ishibuchi received the B.S. and M.S. degrees from Kyoto University in 1985 and 1987, respectively, and the Ph.D. degree from Osaka Prefecture University in 1992. He was with Osaka Prefecture University in 1987-2017. Since April 2017, he is a Chair Professor at Southern University of Science and Technology. He received an IEEE CIS Fuzzy Systems Pioneer Award in 2019, and an IEEE Trans. on Evolutionary Computation Outstanding Paper Award in 2020. He also received Best Paper Awards from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011, ACIIDS 2015, GECCO 2017, GECCO 2018, EMO 2019, GECCO 2020 and GECCO 2021. He was IEEE CIS Vice President in 2010-2013, and Editor-in-Chief of IEEE Computational Intelligence Magazine in 2014-2019. Currently he is AdCom Member of IEEE CIS, IEEE CIS Distinguished Lecturer, Program Chair of IEEE SSCI 2022 (Singapore), General Chair of IEEE WCCI 2024 (Yokohama), and Associate Editor of IEEE Trans. on Evolutionary Computation, IEEE Trans. on Cybernetics, and IEEE Access. He is an IEEE Fellow.