Repository hosting data and select machine learning examples from the paper "Machine Learning Assisted Hyperparameter Tuning for Optimization" presented in IEEE AP-S 2024 in Florence, Italy, July 14-19, 2024.
Redirected from PSO_datacollection to include updates and differentiate between conference presentations.
- Requirements
- How to Use
- Datasets
- Tutorials and Examples
- Code Repositories
- Resources
- Related Publications
- How to Cite
-
The main example is designed to run in Google Colab with Jupyter Notebook. You must have a Google account to run these as-is. The Jupyter Notebook file(s) in this repository run individually. Library requirements for each tutorial and example may vary. Refer to a tutorial or example for the specific requirements.
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Linked repositories featuring optimizers have their own requirements, and will require a Python environment.
- To run, you will need to create a folder in your Google Drive, and add the dataset and all tutorials into the same folder.
- The optimizer repositories were designed to run locally in an IDE. As such, they will not run in Jupyter Notebook as-is and some modification is needed.
To be added closer to the conference
Optimizers are described in their individual repositories in order to keep the development information up to date.
| Base Optimizer | Alternate Version | Quantum-Inspired Optimizer | Surrogate Model Variations |
|---|---|---|---|
| pso_python | pso_basic | pso_quantum | |
| cat_swarm_python | sand_cat_python | cat_swarm_quantum | |
| chicken_swarm_python | 2015 improved chicken swarm | chicken_swarm_quantum | |
| sweep_python | *alternates in base repo | - | - |
| bayesian optimization_python | - | - | *interchangeable surrogate models included in base repo |
| multi_glods_python | - | - |
L. Linkous, J. Lundquist, M. Suche, and E. Topsakal, "Machine Learning Assisted Hyperparameter Tuning for Optimization," 2024 IEEE International Symposium on Antennas and Propagation and ITNC-USNC-URSI Radio Science Meeting, Florence, Italy, July 14-19, 2024.
L. Linkous and E. Topsakal, "Machine Learning Assisted Optimization Methods for Automated Antenna Design," 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2024, pp. 377-378, doi: 10.23919/USNC-URSINRSM60317.2024.10464597.
Data and examples in this repository are from:
L. Linkous, J. Lundquist, M. Suche, and E. Topsakal, "Machine Learning Assisted Hyperparameter Tuning for Optimization," 2024 IEEE International Symposium on Antennas and Propagation and ITNC-USNC-URSI Radio Science Meeting, Florence, Italy, July 14-19, 2024.