IEEE VIC CIS Talk on Fair Performance Comparison of Evolutionary Multi-Objective Optimization Algorithms

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Category: Distinguished Lecturer Program (DLP)

Title: Fair Performance Comparison of Evolutionary Multi-Objective Optimization Algorithms
Speaker: Prof Hisao Ishibuchi (IEEE Distinguished Speaker, IEEE Fellow), Southern University of Science and Technology, Shenzhen, China
Location: RMIT University and Virtual (Zoom) – refreshment starts at 2.30 pm
Time: 3.00 – 4.00 pm (AEST) Monday 30th October 2023
For further details, contact: Malka N. Halgamuge, Chair VIC CIS (
VIC CIS Chapter website:

This is a part of the IEEE Victorian Computational Intelligence Society (CIS) series of talks. The online delivery is kindly hosted by IEEE Victorian Section.

  Date and Time




  • Date: 30 Oct 2023
  • Time: 03:00 PM to 04:00 PM
  • All times are (UTC+11:00) Canberra
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  • RMIT University
  • Melbourne, Victoria
  • Australia
  • Building: 80

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  • Co-sponsored by Swinburne University of Technology

  • Co-sponsored by IEEE VIC CIS Chapter; IEEE VIC Section
  • Starts 15 September 2023 08:21 AM
  • Ends 30 October 2023 03:21 PM
  • All times are (UTC+11:00) Canberra
  • No Admission Charge


Prof Hisao Ishibuchi Prof Hisao Ishibuchi of Southern University of Science and Technology, Shenzhen, China


Fair Performance Comparison of Evolutionary Multi-Objective Optimization Algorithms

Evolutionary multi-objective optimization (EMO) has been a very active research area in recent years. Almost every year, new EMO algorithms are proposed. When a new EMO algorithm is proposed, computational experiments are conducted in order to compare its performance with existing algorithms. Then, experimental results are summarized and reported as a number of tables together with statistical significance test results. Those results usually show higher performance of the new algorithm than existing algorithms. However, fair performance comparison of different EMO algorithms is not easy since the evaluated performance of each algorithm usually strongly depends on experimental settings. In this seminar, we focus on the settings related to the following four issues: (i) termination condition specification, (ii) population size specification, (iii) performance indicator choice, (iv) test problem choice. First, we clearly demonstrate that each of these issues has strong effects on performance comparison results. Then, we discuss how to handle each of these issues for fair performance comparison. These discussions aim to encourage the future development of the EMO research field without focusing too much on the development of overly-specialized new algorithms in a specific setting. Finally, we suggest some promising future research topics related to each issue.


Hisao Ishibuchi is a Chair Professor at Southern University of Science and Technology, China. He was the IEEE Computational Intelligence Society (CIS) Vice-President for Technical Activities in 2010-2013 and the Editor-in-Chief of IEEE Computational Intelligence Magazine in 2014-2019. Currently he is an IEEE CIS Administrative Committee Member, an IEEE CIS Distinguished Lecturer, and an Associate Editor of several journals such as IEEE Transactions on Cybernetics and ACM Computing Surveys. He is also General Chair of IEEE WCCI 2014. He received a Fuzzy Systems Pioneer Award from IEEE CIS in 2019, an Outstanding Paper Award from IEEE Transactions on Evolutionary Computation in 2020, and Best Paper Awards from FUZZ-IEEE 2009, 2011, EMO 2019, and GECCO 2004, 2017, 2018, 2020, 2021. He also received a JSPS prize in 2007. He is an IEEE Fellow.

Address:Shenzhen, China

F2F & Virtual

To join the meeting, please come to RMIT University or use the Zoom details in the description - refreshment starts at 2.30 pm.

Please do not hesitate to contact the host if you have any queries (Dr Malka N. Halgamuge,