TurboGP: A flexible and advanced python based GP library

Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante
8/31/2023
cs.NEcs.LG

No official code found. Best implementation: l1n0b1/TurboGP (36 stars, GPL-3.0, last commit 2024-10-07).

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.

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Code Implementations(1)

l1n0b1/TurboGPCommunity100%
GPL-3.0

Genetic Programming library in Python

364Python, Jupyter NotebookDec 31, 20202 years agoGPL-3.0
evolutionary-algorithmsgenetic-programmingmachine-learningpython3

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Cite this paper

@article{rodriguezcoayahuitl2023turbogp,
  title  = {TurboGP: A flexible and advanced python based GP library},
  author = {Lino Rodriguez-Coayahuitl and Alicia Morales-Reyes and Hugo Jair Escalante},
  year   = {2023},
  eprint = {2309.00149},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.00149v1}
}

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