A4 Article in conference proceedings
An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods (2021)


Afsar, B., Miettinen, K., & Ruiz, A. B. (2021). An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods. In H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, & A. Zhou (Eds.), Evolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 619-631). Springer. Lecture notes in computer science, 12654. https://doi.org/10.1007/978-3-030-72062-9_49


JYU authors or editors


Publication details

All authors or editorsAfsar, Bekir; Miettinen, Kaisa; Ruiz, Ana B.

Parent publicationEvolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings

Parent publication editorsIshibuchi, Hisao; Zhang, Qingfu; Cheng, Ran; Li, Ke; Li, Hui; Wang, Handing; Zhou, Aimin

Place and date of conferenceShenzhen, China28-31.3.2021

ISBN978-3-030-72061-2

eISBN978-3-030-72062-9

Journal or seriesLecture notes in computer science

ISSN0302-9743

eISSN1611-3349

Publication year2021

Number in series12654

Pages range619-631

PublisherSpringer

Place of PublicationCham

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.1007/978-3-030-72062-9_49

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/74854


Abstract

Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phase. We demonstrate the ADM by comparing interactive versions of RVEA and NSGA-III on benchmark problems with up to 9 objectives. The experiments show that our ADM is efficient and allows repetitive testing to compare interactive evolutionary methods in a meaningful way.


Keywordsdecision makingoptimisationmulti-objective optimisationmultivariable methods

Free keywordsdecision making; aspiration levels; performance comparison; many-objective optimization; interactive methods


Contributing organizations


Related projects


Ministry reportingYes

Reporting Year2021

JUFO rating1


Last updated on 2024-26-03 at 09:19