Comparison
devol vs hyperband
Verdict
Pick devol if devolution of neural network architectures through genetic algorithms in Keras for automating design; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Markdown twin · devol alternatives · hyperband alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | devol | hyperband |
|---|---|---|
| Maintenance | Dormant (1166d since push) As of 2w · github_public_v1 | Dormant (2910d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- devol
- Genetic neural architecture search for deep learning models
- hyperband
- Tuning hyperparams fast with Hyperband
Stars
- devol
- 951
- hyperband
- 599
Forks
- devol
- 114
- hyperband
- 73
Open issues
- devol
- 7
- hyperband
- 9
Language
- devol
- Python
- hyperband
- Python
Adopt for
- devol
- Devolution of neural network architectures through genetic algorithms in Keras for automating design.
- hyperband
- Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Persona
- devol
- -
- hyperband
- -
Runtime
- devol
- -
- hyperband
- -
License
- devol
- MIT
- hyperband
- Other
Last pushed
- devol
- May 25, 2023
- hyperband
- Aug 15, 2018
Categories
- devol
- Model Training
- hyperband
- Model Training
Trust and health
Days since push
- devol
- 1166d
- hyperband
- 2910d
Open issues (now)
- devol
- 7
- hyperband
- 9
Full report
- devol
- Trust report
- hyperband
- Trust report
Choose devol if…
- License: devol is MIT, hyperband is Other.
- Pricing: Available under MIT license meaning it is free for personal and commercial use but the author cannot be held responsible or liable from damages caused by using DEvol..
- Tags unique to devol: automl, computer-vision, deep-learning, genetic-algorithm.
- Use DEvol when you need an early proof-of-concept tool to automate the design of neural network architectures with limited parameters, focusing specifically on classification problems.
When NOT to use devol
- Avoid using DEvol in situations requiring deep or highly complex architectures due to the significant computational expense associated with evolutionary search over such a large parameter space.
- Do not use if you lack the infrastructure for parallel processing or do not want to optimize for shorter training epochs, as this can affect model accuracy and fitness evaluations.
Choose hyperband if…
- License: hyperband is Other, devol is MIT.
- Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, regression.
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
When NOT to use hyperband
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules.
- Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (joeddav/devol) · observed Aug 4, 2026
- GitHub forks (joeddav/devol) · observed Aug 4, 2026
- Last push (joeddav/devol) · observed May 25, 2023
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zygmuntz/hyperband) · observed Aug 4, 2026
- GitHub forks (zygmuntz/hyperband) · observed Aug 4, 2026
- Last push (zygmuntz/hyperband) · observed Aug 15, 2018
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: devol 951 · hyperband 599 (synced Aug 4, 2026).
Common questions
- What is the difference between devol and hyperband?
- devol: Genetic neural architecture search for deep learning models. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories.
- When should I choose devol over hyperband?
- Choose devol over hyperband when License: devol is MIT, hyperband is Other; Pricing: Available under MIT license meaning it is free for personal and commercial use but the author cannot be held responsible or liable from damages caused by using DEvol.; Tags unique to devol: automl, computer-vision, deep-learning, genetic-algorithm; Use DEvol when you need an early proof-of-concept tool to automate the design of neural network architectures with limited parameters, focusing specifically on classification problems.
- When should I choose hyperband over devol?
- Choose hyperband over devol when License: hyperband is Other, devol is MIT; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, regression; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- When should I avoid devol?
- Avoid using DEvol in situations requiring deep or highly complex architectures due to the significant computational expense associated with evolutionary search over such a large parameter space. Do not use if you lack the infrastructure for parallel processing or do not want to optimize for shorter training epochs, as this can affect model accuracy and fitness evaluations.
- When should I avoid hyperband?
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
- Is devol or hyperband more popular on GitHub?
- devol has more GitHub stars (951 vs 599). Stars measure visibility, not whether either tool fits your constraints.
- Are devol and hyperband open source?
- Yes - both are open-source projects on GitHub (devol: MIT, hyperband: Other).
- Where can I find alternatives to devol or hyperband?
- GraphCanon lists graph-backed alternatives at devol alternatives and hyperband alternatives (devol markdown twin, hyperband markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, devol or hyperband?
- devol: Dormant. hyperband: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for devol and hyperband?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: devol trust report; hyperband trust report.