Hyperparameter optimization (HPO) is a critical yet computationally intensive task in modern neural network design, particularly as model architectures and search spaces increase in complexity. Classical methods such as grid search and random search are often inefficient in high-dimensional spaces, while Bayesian optim...
Jacek Tyszkiewicz, Paweł Kolendo, Wojciech Chmiel· International Conference on...· 0 citations
The method begins with generation of candidate algorithms, followed by LLM-guided selection of impactful operators, iterative construction of specialized operator variants, and final synthesis of an adaptive optimizer.
Paweł Kolendo, Michal Pluháček· GECCO Companion· 0 citations
These findings suggest that direct configuration prediction provides a robust approach despite ambiguity in the parameter space, and are compared to a regression-based performance prediction model, a multiple-choice model that treats near-optimal configurations as valid targets, and a baseline single-label model.
Paweł Kolendo, Wojciech Chmiel, J. Kwiecien· International Conference on...· 0 citations
Findings indicate that while LLM-based performance prediction is not yet a reliable substitute for benchmarking, it shows potential as a complementary pre-screening tool in iterative algorithm design, particularly in settings where code modifications follow predictable patterns.
Michal Pluháček, Paweł Kolendo, Krzysztof Tylka-Suleja et al.· GECCO Companion· 0 citations
