1 citations · 1 across the 8 of their papers we have counts for
11 papers · 1 filter
Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment
Francesca Da Ros, Tarik Začiragić, 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…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice
Kirill Antonov, Teus Tukker, Tiago Botari +3
Designing high-performance optical lenses entails exploring a high-dimensional, tightly constrained space of surface curvatures, glass choices, element thicknesses, and spacings. I…
Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
Niki van Stein, Haoran Yin, Anna V. Kononova +2
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Lan…
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
Haoran Yin, Anna V. Kononova, Thomas Bäck +1
We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic…
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large Language Models (LLMs) have demonstrated great promise in generating code, especially when used inside an evolutionary computation framework to iteratively optimize the gener…