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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.
arXiv:2605. 04267v2 Announce Type: replace Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting decision-maker preferences that identify the relevant region of the Pareto set.
arXiv:2506. 21887v2 Announce Type: replace Abstract: High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations.
Black-box combinatorial optimization requires systematically identifying high-quality solutions under a limited evaluation budget, yet the unknown objective function provides little guidance for deciding where the search should explore next. We introduce SCOPE, a general framework for Synthetic Conditional Objectives for Policy Evolution in Black-Box Combinatorial Optimization.
arXiv:2610.23062v1 Announce Type: new Abstract: Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria. In practice, how...
arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.