Realistic Utility Functions Prove Difficult for State-of-the-Art Interactive Multiobjective Algorithms

Code and datasets for:

Seyed Mahdi Shavarani, Manuel López-Ibáñez, and Joshua Knowles. Realistic Utility Functions Prove Difficult for State-of-the-Art Interactive Multiobjective Optimization Algorithms. In Genetic and Evolutionary Computation Conference (GECCO ’21), July 10–14, 2021, Lille, France. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3449639.3459373

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.