# EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

arXiv: https://arxiv.org/abs/2207.10367 · HTML: https://mlsanity.com/paper/2207.10367/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Evolution of Activation Functions for Deep Learning-Based Image Classification](https://mlsanity.com/paper/2206.12089/) (2022)
- [Evolving Context-Aware Recommender Systems With Users in Mind](https://mlsanity.com/paper/2007.15409/) (2020)
- [Binary and Multinomial Classification through Evolutionary Symbolic Regression](https://mlsanity.com/paper/2206.12706/) (2022)

## Built on by (most cited first, 4 of 4)

- [Scalable Neural Symbolic Regression using Control Variables](https://mlsanity.com/paper/2306.04718/) (2023)
- [Classy Ensemble: A Novel Ensemble Algorithm for Classification](https://mlsanity.com/paper/2302.10580/) (2023)
- [A Melting Pot of Evolution and Learning](https://mlsanity.com/paper/2306.04971/) (2023)
- [Fitness Approximation through Machine Learning](https://mlsanity.com/paper/2309.03318/) (2023)

## Abstract (arXiv)

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries.