A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Afsar, Bekir; Miettinen, Kaisa; Ruiz, Ana B.

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

Emojulkaisun toimittajat: Ishibuchi, Hisao; Zhang, Qingfu; Cheng, Ran; Li, Ke; Li, Hui; Wang, Handing; Zhou, Aimin

Konferenssin paikka ja aika: Shenzhen, China, 28-31.3.2021

ISBN: 978-3-030-72061-2

eISBN: 978-3-030-72062-9

Lehti tai sarja: Lecture notes in computer science

ISSN: 0302-9743

eISSN: 1611-3349

Julkaisuvuosi: 2021

Sarjan numero: 12654

Artikkelin sivunumerot: 619-631

Kustantaja: Springer

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/74854


Tiivistelmä

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.


YSO-asiasanat: päätöksenteko; optimointi; monitavoiteoptimointi; monimuuttujamenetelmät

Vapaat asiasanat: decision making; aspiration levels; performance comparison; many-objective optimization; interactive methods


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2021

Alustava JUFO-taso: 1


Viimeisin päivitys 2021-08-12 klo 08:32