Mar 25, 2017 · The proposed approach offers a convenient method for the DM to achieve a posteriori selection based on pairwise choices of alternative solutions ...
A preference-based learning approach is employed to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the ...
Aug 19, 2024 · This paper proposes a novel framework called preference prediction-based evolutionary multi-objective optimization (PP-EMO).
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Nov 21, 2024 · To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, ...
Nov 5, 2024 · Preference-based evolutionary multi-objective optimization (PBEMO) methods involve optimization (explore the space), consultation (learn human ...
To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects ...
The paper presents a new tradeoff-based EMO approach utilizing configurable weight intervals assigned to all objectives.
Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the ...
Oct 22, 2024 · In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a ...
In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM's best solution among the candidate solutions on the Pareto-front, generated ...