evobandits
cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.5%) to scientific vocabulary
Keywords
Repository
cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems
Basic Info
- Host: GitHub
- Owner: EvoBandits
- License: apache-2.0
- Language: Rust
- Default Branch: main
- Homepage: https://evobandits.github.io/EvoBandits/
- Size: 473 KB
Statistics
- Stars: 7
- Watchers: 3
- Forks: 1
- Open Issues: 16
- Releases: 6
Topics
Metadata Files
README.md
EvoBandits is a cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems.
EvoBandits (Evolutionary Multi-Armed Bandits) is an innovative optimization algorithm designed to tackle stochastic problems efficiently. EvoBandits offers a reinforcement learning-based approach to solving complex, large-scale optimization issues by combining genetic algorithms with multi-armed bandit mechanisms. Whether you're working in operations research, machine learning, or data science, EvoBandits provides a robust, scalable solution for optimizing your stochastic models.
Usage
To install EvoBandits:
bash
pip install evobandits
```python from evobandits import GMAB
def test_function(number: list) -> float: # your function here
if name == 'main': bounds = [(-5, 10), (-5, 10)] algorithm = GMAB(testfunction, bounds) ntrials = 10000 result = algorithm.optimize(n_trials) print(result) ```
Contributing
Pull requests are welcome. For major changes, please open a discussion first to talk about what you'd like to change.
License
EvoBandits is licensed under the Apache-2.0 license (LICENSE or https://opensource.org/licenses/apache-2-0).
Credit
Deniz Preil wrote the initial EvoBandits prototype in C++, which Timo Kühne and Jonathan Laib rewrote. Timo Kühne ported to Rust, which is now the backend. Felix Würmseher added the Python frontend.
Citing EvoBandits
If you use EvoBandits in your research, please cite the following paper:
Preil, D., & Krapp, M. (2024). Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization. IEEE Transactions on Evolutionary Computation.
Owner
- Name: EvoBandits
- Login: EvoBandits
- Kind: organization
- Repositories: 1
- Profile: https://github.com/EvoBandits
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Preil"
given-names: "Deniz"
orcid: "https://orcid.org/0000-0001-7928-7267"
- family-names: "Krapp"
given-names: "Michael"
orcid: "https://orcid.org/0009-0001-3003-6747"
title: "Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization"
version: 0.1
url: "https://github.com/EvoBandits/EvoBandits"
preferred-citation:
type: article
authors:
- family-names: "Preil"
given-names: "Deniz"
orcid: "https://orcid.org/0000-0001-7928-7267"
- family-names: "Krapp"
given-names: "Michael"
orcid: "https://orcid.org/0009-0001-3003-6747"
journal: "IEEE Transactions on Evolutionary Computation"
title: "Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization."
year: 2024
GitHub Events
Total
- Create event: 60
- Issues event: 37
- Release event: 7
- Watch event: 3
- Delete event: 41
- Issue comment event: 28
- Push event: 226
- Pull request review comment event: 53
- Pull request review event: 101
- Pull request event: 69
Last Year
- Create event: 60
- Issues event: 37
- Release event: 7
- Watch event: 3
- Delete event: 41
- Issue comment event: 28
- Push event: 226
- Pull request review comment event: 53
- Pull request review event: 101
- Pull request event: 69
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 28
- Total pull requests: 96
- Average time to close issues: 21 days
- Average time to close pull requests: 4 days
- Total issue authors: 2
- Total pull request authors: 4
- Average comments per issue: 0.71
- Average comments per pull request: 0.36
- Merged pull requests: 80
- Bot issues: 0
- Bot pull requests: 7
Past Year
- Issues: 28
- Pull requests: 96
- Average time to close issues: 21 days
- Average time to close pull requests: 4 days
- Issue authors: 2
- Pull request authors: 4
- Average comments per issue: 0.71
- Average comments per pull request: 0.36
- Merged pull requests: 80
- Bot issues: 0
- Bot pull requests: 7
Top Authors
Issue Authors
- fwuermseher (20)
- tnkuehne (8)
Pull Request Authors
- fwuermseher (45)
- tnkuehne (42)
- dependabot[bot] (7)
- jonathanlaib (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 349 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 6
- Total maintainers: 3
pypi.org: evobandits
Optimization algorithm combining genetic algorithms and multi-armed bandits for stochastic problems
- Documentation: https://evobandits.github.io/EvoBandits/
- License: apache-2.0
-
Latest release: 0.0.6
published 8 months ago
Rankings
Maintainers (3)
Dependencies
- actions/checkout v3 composite
- PyO3/maturin-action v1 composite
- actions/checkout v4 composite
- actions/setup-python v5 composite
- actions/upload-artifact v4 composite
- uraimo/run-on-arch-action v2 composite
- scikit-learn *