TurboGP: A flexible and advanced python based GP library
This provides a flexible tool for researchers and practitioners in machine learning, though it is incremental as it builds on existing GP methods with new features.
The authors introduced TurboGP, a Python-based Genetic Programming library designed for machine learning tasks, featuring modern implementations like island and cellular population schemes, various genetic operations, and native support for different GP nodes to handle diverse data sources.
We introduce TurboGP, a Genetic Programming (GP) library fully written in Python and specifically designed for machine learning tasks. TurboGP implements modern features not available in other GP implementations, such as island and cellular population schemes, different types of genetic operations (migration, protected crossovers), online learning, among other features. TurboGP's most distinctive characteristic is its native support for different types of GP nodes to allow different abstraction levels, this makes TurboGP particularly useful for processing a wide variety of data sources.