A genetic programming system for regression and classification

View the Project on GitHub


ellyn is fast because it uses a c++ library to do most of the computation. However, once you have it installed, you can use it just like you would any other scikit-learn estimator, which makes it easy to do cross validation, ensemble learning, or to build any other kind of ML pipeline design. Follow the installation guide to get it up and running.


These instructions are written for an anaconda3 default python installation, but you can easily modify the paths to point to your installation.


The hairiest part of the installation is getting boost installed with boost python. If you don’t have boost yet, run

wget "https://sourceforge.net/projects/boost/files/boost/1.62.0/boost_1_62_0.tar.gz" 
tar -xzf boost_1_62_0.tar.gz # install boost
# navigate to the installation folder
cd boost_1_62_0 
# bootstrap boost python builder
./boostrap.sh --with-libraries=python --with-python-root=/home/$USER/anaconda3 
# add symbolic link to python3.5 include file
ln -s /home/$USER/anaconda/include/python3.5m /home/$USER/anaconda/include/python3.5
# build boost python
./b2 --with-python


eigen is a sweet matrix library for c++. If you have deb / ubuntu, you can install it via

sudo apt-get install libeigen3-dev

otherwise, install it via their website. on linux systems it should be in /usr/include/eigen3, but if it’s somewhere else, edit ellen/Makefile to point to it.


Now you can build the c++ library ellen. Go to this repo in terminal. Then type

cd ellyn/ellen


In a python script, import ellyn:

from ellyn import ellyn

ellyn uses the same nomenclature as sklearn supervised learning modules. You can initialize a few learner in python as:

learner = ellyn()

or specify the generations, population size and selection algorithm as:

learner = ellyn(generations = 100, popsize = 25, selection = 'lexicase')

Given a set of data with variables X and target Y, fit ellyn using the fit() method:


You have now learned a model for your data. Predict your model’s response on a new set of variables as

y_pred = learner.predict(X_unseen)

Call ellyn from the terminal as

python -m ellyn.ellyn data_file_name -g 100 -p 50 -sel lexicase

try python -m ellyn.ellyn --help to see options.

GP options

ellyn uses a stack-based, syntax-free, linear genome for constructing candidate equations.

Selection/Survival options


Parameter learning


ellyn has been used in several publications. Cite the one that best represents your use case, or you can cite my dissertation if you’re not sure.