https://github.com/ctlab/gadma
Genetic Algorithm for Demographic Model Analysis
https://github.com/ctlab/gadma
Last synced: 8 months ago
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Genetic Algorithm for Demographic Model Analysis
- Host: GitHub
- URL: https://github.com/ctlab/gadma
- Owner: ctlab
- License: other
- Created: 2018-05-15T10:43:12.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2025-10-08T20:50:11.000Z (9 months ago)
- Last Synced: 2025-10-26T14:58:02.881Z (8 months ago)
- Language: Python
- Size: 365 MB
- Stars: 52
- Watchers: 4
- Forks: 16
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GADMA 
[](https://gadma.readthedocs.io/en/latest/?badge=latest) [](https://github.com/ctlab/GADMA/actions) [](https://codecov.io/gh/ctlab/GADMA) [](https://pypistats.org/packages/gadma)
Welcome to GADMA v2!
GADMA implements methods for automatic inference of the joint demographic history of multiple populations from the genetic data.
**GADMA is a command-line tool**. Basic pipeline presents a series of launches of the global search algorithm followed by the local search optimization.
GADMA provides two types of demographic inference: 1) for user-specified model of demographic history or a custom model, 2) automatic inference for the model with specified structure (up to three populations, see more [here](https://gadma.readthedocs.io/en/latest/user_manual/set_model/set_model_struct.html)).
GADMA provides choice of several engines of demographic inference. This list will be extended in the future. Available engines and maximum number of supported populations for custom model:
* [∂a∂i](https://bitbucket.org/gutenkunstlab/dadi/) (up to 5 populations)
* [*moments*](https://bitbucket.org/simongravel/moments/) (up to 5 populations)
* [*momi2*](https://github.com/popgenmethods/momi2/) (up to ∞ populations)
* [*momentsLD*](https://bitbucket.org/simongravel/moments/) - extenstion of *moments* (up to 5 populations)
More information about engines see [here](https://gadma.readthedocs.io/en/latest/user_manual/set_engine.html).
GADMA features various optimization methods ([global](https://gadma.readthedocs.io/en/latest/api/gadma.optimizers.html#global-optimizers-list) and [local](https://gadma.readthedocs.io/en/latest/api/gadma.optimizers.html#local-optimizers-list) search algorithms) which may be used for [any general optimization problem](https://gadma.readthedocs.io/en/latest/api_examples/optimization_example.html).
Two global search algorithms are supported in GADMA:
* Genetic algorithm — the most common choice of optimization,
* Bayesian optimization — for demographic inference with time-consuming evaluations, e.g. for four and five populations using *moments* or ∂a∂i.
GADMA is developed in Computer Technologies laboratory at ITMO University under the supervision of [Vladimir Ulyantsev](https://ulyantsev.com/) and Pavel Dobrynin. The principal maintainer is [Ekaterina Noskova](http://enoskova.me/) (ekaterina.e.noskova@gmail.com)
**GADMA is now of version 2!** See [Changelog](https://gadma.readthedocs.io/en/latest/changelogs.html).
### Documentation
Please see [documentation](https://gadma.readthedocs.io) for more information including installation instructions, usage, examples and API.
## Getting help
[F.A.Q.](https://gadma.readthedocs.io/en/latest/faq.html)
Please don't be afraid to contact me for different problems and offers via email ekaterina.e.noskova@gmail.com. I will be glad to answer all questions.
Also you are always welcome to [create an issue](https://github.com/ctlab/GADMA/issues) on the GitHub page of GADMA with your question.
## Citations
Please see full list of citations in [documentation](https://gadma.readthedocs.io/en/latest/citations.html).
If you use GADMA in your research please cite:
Ekaterina Noskova, Vladimir Ulyantsev, Klaus-Peter Koepfli, Stephen J O’Brien, Pavel Dobrynin, GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data, *GigaScience*, Volume 9, Issue 3, March 2020, giaa005,
If you use GADMA2 in your research please cite:
Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, and Vladimir Ulyantsev, GADMA2: more efficient and flexible demographic inference from genetic data, *GigaScience*, Volume 12, August 2023, giad059,
If you use Bayesian optimization please cite:
Ekaterina Noskova and Viacheslav Borovitskiy, Bayesian optimization for demographic inference, *G3 Genes|Genomes|Genetics*, Volume 13, Issue 7, July 2023, jkad080,