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EC-KitY is a Python tool kit for doing evolutionary computation. It is scikit-learn-compatible and is distributed under the GNU General Public License v3.0. Currently we have implemented 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:

  • 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

git clone

For basic package installation: pip install -r requirements.txt


After cloning the project, navigate to the doc\api folder and open eckity.html file. This will open an html page from which you can access the documentation of all the modules.

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


There are 4 tutorials 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.

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

We are working on a paper that describes EC-KitY. For now, here is a table comparing EC-KitY with 8 other libraries: image


Moshe Sipper, Achiya Elyasaf, Itai Tzruia, Tomer Halperin


Citations are always appreciated 😊:

    author = {Sipper, Moshe and Halperin, Tomer and Tzruia, Itai and  Elyasaf, Achiya},
    title = {{EC-KitY}: Evolutionary Computation Tool Kit in {Python}},
    journal = {},
    volume = {},
    pages = {},
    year = {2022},
    note = {in preparation}

    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{} }