EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration

Moshe Sipper; Tomer Halperin; Itai Tzruia; Achiya Elyasaf

doi:https://doi.org/10.1016/j.softx.2023.101381

EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration

Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	American English
Article number	101381
Journal	SoftwareX
Volume	22
DOIs	https://doi.org/10.1016/j.softx.2023.101381
State	Published - 1 May 2023

Keywords

Evolutionary algorithms
Evolutionary computation
Genetic programming
Machine learning
Scikit-learn

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications

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https://doi.org/10.1016/j.softx.2023.101381

Cite this

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title = "EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration",

abstract = "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.",

keywords = "Evolutionary algorithms, Evolutionary computation, Genetic programming, Machine learning, Scikit-learn",

author = "Moshe Sipper and Tomer Halperin and Itai Tzruia and Achiya Elyasaf",

note = "Funding Information: This research was partially supported by grant #2714/19 from the Israeli Science Foundation and by the Israeli Smart Transportation Research Center (ISTRC) and by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

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doi = "https://doi.org/10.1016/j.softx.2023.101381",

language = "American English",

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AU - Halperin, Tomer

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AU - Elyasaf, Achiya

N1 - Funding Information: This research was partially supported by grant #2714/19 from the Israeli Science Foundation and by the Israeli Smart Transportation Research Center (ISTRC) and by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel. Publisher Copyright: © 2023 The Author(s)

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KW - Evolutionary algorithms

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EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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