Realistic utility functions prove difficult for state-of-the-art interactive multiobjective optimization algorithms

Seyed Mahdi Shavarani, Manuel López-Ibáñez, Joshua Knowles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Improvements to the design of interactive Evolutionary Multiobjective Algorithms (iEMOAs) are unlikely without quantitative assessment of their behaviour in realistic settings. Experiments with human decision-makers (DMs) are of limited scope due to the difficulty of isolating individual biases and replicating the experiment with enough subjects, and enough times, to obtain confidence in the results. Simulation studies may help to overcome these issues, but they require the use of realistic simulations of decision-makers. Machine decision-makers (MDMs) provide a way to carry out such simulation studies, however, studies so far have relied on simple utility functions. In this paper, we analyse and compare two state-of-the-art iEMOAs by means of a MDM that uses a sigmoid-shaped utility function. This sigmoid utility function is based on psychologically realistic models from behavioural economics, and replicates several realistic human behaviours. Our findings are that, on a variety of well-known benchmarks with two and three objectives, the two iEMOAs do not consistently recover the most-preferred points. We hope that these findings provide an impetus for more directed design and analysis of future iEMOAs.

Original languageEnglish
Title of host publicationGECCO '21
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference
EditorsFrancisco Chicano
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450383509
Publication statusPublished - 26 Jun 2021
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 10 Jul 202114 Jul 2021

Publication series

NameGenetic and Evolutionary Computation Conference (GECCO)


Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
CityVirtual, Online

Bibliographical note

Funding Information:
M. López-Ibáñez is a “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Ministry of Science and Innovation of the Spanish Government.

Publisher Copyright:
© 2021 ACM.


  • Design of experiments
  • Interactive evolutionary multi-objective optimization
  • Machine decision maker

ASJC Scopus subject areas

  • Genetics
  • Computational Mathematics


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