← Back to all news
Hugging Face Trending Papers

Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design

Read the original on Hugging Face Trending Papers →

This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters. The framework integrates three fidelity levels: high-fidelity Fast Fourier Transform (FFT) homogenization for validation, a medium-fidelity 3D convolutional neural network surrogate for rapid property evaluation, and a low-fidelity Gaussian process (GP) surrogate within a Bayesian optimization (BO) framework to guide the hyperparameter search.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

Related stories

arXiv Machine Learning
Jul 14

Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems

arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.

By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
benchmarks
More like this →
arXiv AI
Jul 13

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.

By Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas B\"ack, Florian Neukart
llmsbenchmarks
More like this →
arXiv Machine Learning
Jul 27

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression

arXiv:2607. 22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.

By Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi
benchmarks
More like this →
arXiv Machine Learning
Jun 18

A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development

arXiv:2606. 19230v1 Announce Type: new Abstract: This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation.

By Samuel Stricker, Claus Wirnsperger, Alessandro Butt\'e, Laura Helleckes, Gonzalo Guill\'en Gos\'albez, Antonio del Rio Chanona, Mehmet Mercang\"oz
More like this →