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.

翻译：我们介绍TurboGP，这是一个完全用Python编写并专门为机器学习任务设计的遗传编程库。TurboGP实现了其他遗传编程实现中不具备的现代特性，例如岛屿和细胞群体方案、不同类型的遗传操作（迁移、保护的交叉操作）、在线学习等。TurboGP最显著的特点是原生支持不同类型的遗传编程节点，允许不同抽象级别的实现，这使得TurboGP在处理各种数据源时特别有用。