TurboGP: A flexible and advanced python based GP library

TL;DR

TurboGP is a versatile Python library for genetic programming tailored for machine learning, featuring modern techniques like island models, cellular schemes, and multi-level node support, enabling flexible data processing.

Contribution

It introduces TurboGP, a Python-based GP library with advanced features and support for various node types, enhancing flexibility and applicability in machine learning tasks.

Findings

Supports multiple population schemes like island and cellular models

Enables online learning with genetic programming

Allows processing of diverse data sources through multi-level nodes

Abstract

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.