GraphCanon updated Sep 30, 2026 · GitHub synced Sep 30, 2026
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Decision brief
Devolution of neural network architectures through genetic algorithms in Keras for automating design.
Good fit when
- 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.
- Consider it if your project involves shallow networks where computational resources are more manageable and parallel training setups can be established for evaluating multiple models.
Avoid when
- 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.
- 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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1223d since push)
- As of Sep 30, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 30, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
pip install devolSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
DEvol is an early proof-of-concept tool for automating the design of neural network architectures using genetic algorithms within Keras.
Capability facts
- Languages
- python
Source: github.language · Sep 30, 2026
Categories
Tags
README
DEvol Deep Neural Network Evolution DEvol (DeepEvolution) is a basic proof of concept for genetic architecture search in Keras. The current setup is designed for classification problems, though this could be extended to include any other output type as well. See for a simple exam...
For agents
This page has a .md twin and JSON over the API.