genepro is a Python library providing a baseline implementation of genetic programming, an evolutionary algorithm specialized to evolve programs.
This library includes a classifier and regressor that are compatible with scitik-learn (see examples of usage below).
Evolving programs are represented as trees. The leaf nodes (also called terminals) of such trees represent some form of input, e.g., a feature for classification or regression, or a type of environmental observation for reinforcement learning. The internal ndoes represent possible atomic instructions, e.g., summation, subtraction, multiplication, division, but also if-then-else or similar programming constructs.
Genetic programming operates on a population of trees, typically initialized at random. Every iteration (called generation), promising trees undergo random modifications (e.g., forms of crossover, mutation, and tuning) that result in a population of offspring trees. This new population is then used for the next generation.
To perform a minimal installation, run:
git clone https://github.com/marcovirgolin/genepro
cd genepro
pip install .
If you'd like to set up a conda environment that includes additional libraries to run the examples in the notebooks, run:
git clone https://github.com/marcovirgolin/genepro
cd genepro
conda env create
conda activate genepro
pip install .
The notebook classification and regression.ipynb shows how to use genepro for classification and regression, via scikit-learn estimators.
These estimators are intended for data sets with a small number of (relevant) features, as the evolved program can be written as a compact (and potentially interpretable) symbolic expression.
...
gen: 38, best of gen fitness: -3010.345, best of gen size: 43
gen: 39, best of gen fitness: -2998.565, best of gen size: 43
gen: 40, best of gen fitness: -2986.394, best of gen size: 45
The negative mean squared error on the test set is -2757.157 (respective R^2 score is 0.546)
Obtained by the (simplified) model:
5*x2 + 3*x3 - x6 + 3*x8 + log(abs(x9)) + log(abs(2*x2 + x8 + 1.44)) - 9.266
Example of output of a symbolic regression model discovered for the Diabetes data set.
The notebook gym.ipynb shows how genepro can be used to evolve a controller for the CartPole-v1 environment of the OpenAI gym library.
(x2 + x3)(x2x3 + x3 + x4 + 1)log(abs(x2))**2log(abs(x3))**2