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.
Many decision-making tasks are inherently multi-objective. Interactive evolutionary multi-objective optimization algorithms (iEMOAs) are particularly well-suited to help the decision-maker find their...
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: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.
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:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.
arXiv:2609.22329v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...
The paper investigates how to create steerable AI models that can balance multiple, sometimes conflicting objectives, a necessity for pluralistic alignment. Using Multi-Objective Direct Preference Optimization (MODPO), the authors examine when a single model can improve two objectives simultaneously and how to cover many trade‑offs without training separate models. They find that two pre‑training measurements predict objective alignment for human‑annotated data but not for AI‑annotated data, and that selecting the nearest trained model or merging parameters can broaden trade‑off coverage, though neither approach consistently matches direct training.
arXiv:2608.28620v1 Announce Type: new Abstract: Preference elicitation is essential for aligning AI systems with human values. Prior approaches (e.g., for organ allocation) often ask stakeholders to...
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
The paper introduces OGC-DPO, an active preference learning framework for aligning large language models with multiple objectives such as helpfulness, harmlessness, and honesty. OGC-DPO treats each response pair and objective as a separate query, using the model’s implicit gradient to gauge informativeness and selecting queries that expand gradient coverage for each objective. Experiments show that OGC-DPO outperforms existing methods at the same annotation cost and reveals that informative conflicts can serve as valuable training signals.