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

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Misc{eckity2022,

• 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",
• howpublished = "arXiv",
• year = "2022",
• volume = "abs/2207.10367",
• month = "22 " # jul,
• keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Evolutionary Computation, Machine Learning, scikit-learn",
• biburl = "https://dblp.org/rec/journals/corr/abs-2207-10367.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.48550/arXiv.2207.10367",
• DOI = "doi:10.48550/arXiv.2207.10367",
• code_url = "https://github.com/ec-kity/ec-kity/",
• size = "6 pages",
• abstract = "EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under GNU General Public License v3.0, 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.",
• notes = "Ben-Gurion University, Beer-Sheva 8410501, Israel",

}

Genetic Programming entries for Moshe Sipper Tomer Halperin Itai Tzruia Achiya Elyasaf

Citations