23 papers
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
Georgios Laskaris, Reuben Brasher, Niki van Stein +3
Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent a…
Lost in Vagueness: Towards Context-Sensitive Standards for Robustness Assessment under the EU AI Act
Roberta Tamponi, Carina Prunkl, Thomas Bäck +1
Robustness is a key requirement for high-risk AI systems under the EU Artificial Intelligence Act (AI Act). However, both its definition and assessment methods remain underspecifie…
MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling
Zhong Li, Qi Huang, Yuxuan Zhu +6
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingl…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors
Qi Huang, Furong Ye, Ananta Shahane +2
Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fi…
Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment
Francesca Da Ros, Tarik ZaÄiragiÄ, Aske Plaat +2
Reproducibility is an important requirement in evolutionary computation, where results largely depend on computational experiments. In practice, reproducibility relies on how algor…