EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration
This provides a tool for researchers and practitioners in evolutionary computation and machine learning to streamline experiments, but it is incremental as it builds on existing EC concepts with new software integration.
The paper introduces EC-KitY, a Python library for evolutionary computation that integrates with scikit-learn, supporting various EC paradigms like genetic algorithms and multi-objective optimization, and includes features for easy experiment setup and comparisons with other libraries.
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.