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Efficient preference learning algorithm for interactive evolutionary multi-objective optimization
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DOI：10.1016/j.swevo.2025.102254.png)
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Abstract
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• We introduce a novel evolution framework for diverse preference-based models. • The framework uniquely balances elasticity, efficiency, and distributional quality. • The method outperforms baseline approaches in robustness, efficiency, and success rates. • The framework leads to better-distributed models and superior objective space coverage. • Ensuring uniform model distribution lets avoiding biased solution assessment.
Keywords:
Preference learning
Pairwise comparisons
Interactive procedures
Evolutionary multi-objective optimization
Uniform sampling
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Cited Papers
Using Choquet integral as preference model in interactive evolutionary multiobjective optimization
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