2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large language models have enabled automated algorithm design (AAD) by generating optimization algorithms directly from natural-language prompts. While evolutionary frameworks such…
How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
Mathieu Cherpitel, Janne Luijten, Thomas Bäck +4
Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneou…
From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
Niki van Stein, Anna V. Kononova, Thomas Bäck
Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterativ…
Reasoning Capabilities of Large Language Models on Dynamic Tasks
Annie Wong, Thomas Bäck, Aske Plaat +2
Large language models excel on static benchmarks, but their ability as self-learning agents in dynamic environments remains unclear. We evaluate three prompting strategies: self-re…