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image PyPI

EC-KitY is a Python tool kit for doing evolutionary computation, and it is scikit-learn compatible.

Currently we have implemented Genetic Algorithm (GA) and tree-based Genetic Programming (GP), but EC-KitY will grow!

EC-KitY is:

  • A comprehensive toolkit for running evolutionary algorithms
  • Written in Python
  • Can work with or without scikit-learn, i.e., supports both sklearn and non-sklearn modes
  • Designed with modern software engineering in mind
  • Designed to support all popular EC paradigms (GA, GP, ES, coevolution, multi-objective, etc').


For the basic evolution mode, EC-KitY requires:

  • Python (>=3.6)
  • Numpy (>=1.14.6)
  • Pandas (>=0.25.0)
  • Overrides (>= 6.1.0)

For sklearn mode, EC-KitY additionally requires:

  • scikit-learn (>=0.24.2)

User installation

pip install eckity


API is available here

(Work in progress - some modules and functions are not documented yet.)


The tutorials are available here, walking you through running EC-KitY both in sklearn mode and in non-sklearn mode.


More examples are in the examples folder. All you need to do is define a fitness-evaluation method, through a SimpleIndividualEvaluator sub-class. You can run the examples with ease by opening this colab notebook.

Basic example (no sklearn)

You can run an EA with just 3 lines of code. The problem being solved herein is simple symbolic regression.

Additional information on this problem can be found in the Symbolic Regression Tutorial.

from eckity.algorithms.simple_evolution import SimpleEvolution
from eckity.subpopulation import Subpopulation
from examples.treegp.non_sklearn_mode.symbolic_regression.sym_reg_evaluator import SymbolicRegressionEvaluator

algo = SimpleEvolution(Subpopulation(SymbolicRegressionEvaluator()))
print(f'algo.execute(x=2,y=3,z=4): {algo.execute(x=2, y=3, z=4)}')

Example with sklearn

The problem being solved herein is the same problem, but in this case we also involve sklearn compatability - a core feature of EC-KitY. Additional information for this example can be found in the Sklearn Symbolic Regression Tutorial.

A simple sklearn-compatible EA run:

from sklearn.datasets import make_regression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

from eckity.algorithms.simple_evolution import SimpleEvolution
from eckity.creators.gp_creators.full import FullCreator
from import create_terminal_set
from eckity.sklearn_compatible.regression_evaluator import RegressionEvaluator
from eckity.sklearn_compatible.sk_regressor import SKRegressor
from eckity.subpopulation import Subpopulation

X, y = make_regression(n_samples=100, n_features=3)
terminal_set = create_terminal_set(X)

algo = SimpleEvolution(Subpopulation(creators=FullCreator(terminal_set=terminal_set),
regressor = SKRegressor(algo)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2), y_train)
print('MAE on test set:', mean_absolute_error(y_test, regressor.predict(X_test)))

Feature comparison

Here's a comparison table. The full paper is available here. image


Moshe Sipper, Achiya Elyasaf, Itai Tzruia, Tomer Halperin


Citations are always appreciated 😊:

author = {Moshe Sipper and Tomer Halperin and Itai Tzruia and Achiya Elyasaf},
title = {{EC-KitY}: Evolutionary computation tool kit in {Python} with seamless machine learning integration},
journal = {SoftwareX},
volume = {22},
pages = {101381},
year = {2023},
url = {},

    author = {Sipper, Moshe and Halperin, Tomer and Tzruia, Itai and  Elyasaf, Achiya},
    title = {{EC-KitY}: Evolutionary Computation Tool Kit in {Python}},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{} }

Sample repos using EC-KitY