arXiv:2606. 12382v1 Announce Type: cross Abstract: The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is a popular and prominent evolutionary algorithm for solving multi-objective optimisation problems.
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arXiv:2609.05992v1 Announce Type: new Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task complet...
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In M...
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:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.
arXiv:2609.14418v1 Announce Type: cross Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple ex...