An Interactive Evolutionary Multiobjective Optimization Algorithm Based on Interpretable Active Preference Learning

The paper introduces an interpretable interactive evolutionary multi-objective optimization algorithm that learns a tree-based preference model through active learning from pairwise comparisons. It extends the survival operator of NSGA-II and NSGA-III to guide the search toward the decision-maker’s preferred region. Experiments show the method outperforms both black-box and other interpretable models in locating the most preferred solution, and a real-life engineering case study demonstrates how the learned model reveals the objectives influencing preferences.

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