--- title: "devol vs hyperband" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/joeddav-devol-vs-zygmuntz-hyperband" tools: ["joeddav-devol", "zygmuntz-hyperband"] --- # devol vs hyperband *GraphCanon updated Aug 4, 2026* ## 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. [devol](https://github.com/joeddav/devol) reports 951 GitHub stars, 114 forks, and 7 open issues, last pushed May 25, 2023. [hyperband](http://fastml.com/tuning-hyperparams-fast-with-hyperband/) has 599 stars, 73 forks, and 9 open issues, last pushed Aug 15, 2018. Figures are from public GitHub metadata via [devol's repository](https://github.com/joeddav/devol) and [hyperband's repository](https://github.com/zygmuntz/hyperband). | | [devol](/tools/joeddav-devol.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Tagline | Genetic neural architecture search for deep learning models | Tuning hyperparams fast with Hyperband | | Stars | 951 | 599 | | Forks | 114 | 73 | | Open issues | 7 | 9 | | Language | Python | Python | | Adopt for | Devolution of neural network architectures through genetic algorithms in Keras for automating design. | Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [devol](/tools/joeddav-devol.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Days since push | 1166d | 2910d | | Open issues (now) | 7 | 9 | | Full report | [trust report](/tools/joeddav-devol/trust.md) | [trust report](/tools/zygmuntz-hyperband/trust.md) | ## Decision facts: devol - **Pricing:** freemium - 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. - **Adopt for:** Devolution of neural network architectures through genetic algorithms in Keras for automating design. ## Decision facts: hyperband - **Adopt for:** Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/joeddav-devol/alternatives) and [hyperband alternatives](/tools/zygmuntz-hyperband/alternatives) ([devol markdown twin](/tools/joeddav-devol/alternatives.md), [hyperband markdown twin](/tools/zygmuntz-hyperband/alternatives.md)), 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](/compare/joeddav-devol-vs-zygmuntz-hyperband.md) 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](/tools/joeddav-devol/trust); [hyperband trust report](/tools/zygmuntz-hyperband/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=joeddav-devol`](/api/graphcanon/graph?tool=joeddav-devol) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_