ArticleScientific reports2023
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Loss function influence on hyperparameter optimization for observational healthcare prediction models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security.Sensors (Basel, Switzerland) · 2026Review
- Comparative Evaluation of Machine Learning and Hyperparameter Optimization Methods for Low-Cost COSensors (Basel, Switzerland) · 2026Article
- Automated deep learning by recurrent hyperparameter optimization.Nature communications · 2026Article
- Automating differentially private tabular data synthesis via Bayesian optimization.Scientific reports · 2026Article
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- AI-driven automation of occlusal surface design in digital prosthodontics: a clinically validated workflow.BMC oral health · 2026Article
- Research on the predicted height of water-conducting fracture zones based on the BO-RFR model and SHAP analysis.Scientific reports · 2026Article
- Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes.Scientific reports · 2026Article
- Explainable Deep Reinforcement Learning for Anomaly Detection in IoT-Enabled Metaverse Healthcare: Toward Trustworthy Cyber Threat Intelligence.Research (Washington, D.C.) · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- A novel method of bayesian genetic optimization on automated hyperparameter tuning.Scientific reports · 2025Article
- Automated Alzheimer's disease detection using active learning model with reinforcement learning and scope loss function.Npj mental health research · 2025Article
- Automated machine learning for classification and regression: A tutorial for psychologists.Behavior research methods · 2025Article
- State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Image steganalysis using active learning and hyperparameter optimization.Scientific reports · 2025Article
- Interpretable real-time monitoring of short-term rockbursts in underground spaces based on microseismic activities.Scientific reports · 2025Article
- An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction.Computational and structural biotechnology journal · 2025Article
- Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband.PloS one · 2025Article
- Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.NPJ systems biology and applications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation-evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation-evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.