On the elicitation of indirect preferences in interactive evolutionary multiple objective optimization

Tomczyk, Michał K.; Kadziński, Miłosz

doi:10.1145/3377930.3389826

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…A recent paper [56] investigates indirect preference elicitation techniques for MOO and specifically examines the selection of comparison solutions for presentation to a DM. Results from this work reveal the benefits of selecting comparisons based on maximising information gain.…”

Section: Related Workmentioning

confidence: 99%

Bayesian preference learning for interactive multi-objective optimisation

Taylor

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference

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This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted periodically during optimisation of the problem and asked to provide their preference over a series of pairwise comparisons of candidate solutions. After each consultation, the most preferred solution is used as the reference point in an appropriate multiobjective optimisation evolutionary algorithm (MOEA). The rationale for using Bayesian optimisation is to identify the most preferred location in the decision search space with the least number of DM queries, thereby minimising DM cognitive burden and fatigue. This enables non-expert DMs to be involved in the optimisation process and make more informed decisions. We further reduce the number of preference queries required, by progressively redefining the Bayesian search space to reflect the MOEA's decision bounds as it converges toward the Pareto Front. We demonstrate how this approach can locate a reference point close to an unknown preferred location on the Pareto Front, of both benchmark and real-world problems with relatively few pairwise comparisons. CCS CONCEPTS• Applied computing → Multi-criterion optimization and decision-making; • Theory of computation → Interactive computation; Active learning;

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Section: Related Workmentioning

confidence: 99%