Python toolkit for continuous Genetic Algorithm optimization.

genetic-algorithm, model-calibration, optimization, optimizer, parameter-estimation, pysb, python3, systems-biology
pip install galibrate==0.4.2



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GAlibrate is a python toolkit that provides an easy to use interface for model calibration/parameter estimation using an implementation of continuous genetic algorithm-based optimization. Its functionality and API were designed to be familiar to users of the PyDREAM, simplePSO, and Gleipnir packages.

Although GAlibrate provides a general framework for running continuous genetic algorithm-based optimizations, it was created with systems biology models in mind. It therefore supplies additional tools for working with biological models in the PySB format.


! Warning
GAlibrate is still under heavy development and may rapidly change.

GAlibrate installs as the galibrate package. It is compatible (i.e., tested) with Python 3.6 and 3.7.

Note that galibrate has the following core dependencies:

pip install

You can install the latest release of the galibrate package using pip sourced from the GitHub repo:

pip install -e git+

However, this will not automatically install the core dependencies. You will have to do that separately:

pip install numpy scipy

conda install

You can install the galibrate package from the blakeaw channel:

conda install -c blakeaw galibrate

NumPy and SciPy dependencies will be automatically installed with this version.

Recommended additional software

The following software is not required for the basic operation of GAlibrate, but provides extra capabilities and features when installed.


GAlibrate includes an implementation of the core genetic algorithm that is written in Cython, which takes advantage of Cython-based optimizations and compilation to accelerate the algorithm. This version of genetic algorithm is used if Cython is installed.


GAlibrate also includes an implementation of the core genetic algorithm that takes advantage of Numba-based JIT compilation and optimization to accelerate the algorithm. This version of genetic algorithm is used if Numba is installed.


PySB is needed to run PySB models, and it is therfore needed if you want to use tools from the galibrate.pysb_utils package.


This project is licensed under the MIT License - see the LICENSE file for details

Documentation and Usage

Quick Overview

Principally, GAlibrate defines the GAO (continuous Genetic Algorithm-based Optimizer ) class,

from galibrate import GAO

which defines an object that can be used setup and run a continuous genetic algorithm-based optimization (i.e., a maximization) of a user-defined fitness function over the search space of a given set of (model) parameters.

Additionally, GAlibrate has a pysb_utils sub-package that provides the galibrate_it module, which defines the GaoIt and GAlibrateIt classes (importable from the pysb_utils package level),

from galibrate.pysb_utils import GaoIt, GAlibrateIt

which create objects that abstract away some of the effort to setup and generate GAO instances for PySB models; examples/pysb_dimerization_model provides some examples for using GaoIt and GAlibrateIt objects. The galibrate_it module can also be called from the command line to generate a template run script for a PySB model,

python -m galibrate.pysb_utils.galibrate_it output_path

which users can then modify to fit their needs.


Additional example scripts that show how to setup and launch Genetic Algorithm runs using GAlibrate can be found under examples.


To report problems or bugs please open a GitHub Issue. Additionally, any comments, suggestions, or feature requests for GAlibrate can also be submitted as a GitHub Issue.


If you use the GAlibrate software in your research, please cite it. You can export the GAlibrate citation in your preferred format from its Zenodo DOI entry.

Also, please cite the following references as appropriate for software used with/via GAlibrate:

Packages from the SciPy ecosystem

These include NumPy and SciPy for which references can be obtained from:


  1. Lopez, C. F., Muhlich, J. L., Bachman, J. A. & Sorger, P. K. Programming biological models in Python using PySB. Mol Syst Biol 9, (2013). doi:10.1038/msb.2013.1