gplearn and pyshgp
These are competitors offering different genetic programming paradigms—gplearn implements symbolic regression and classification through tree-based genetic programming with scikit-learn integration, while pyshgp implements Push, a stack-based language designed for evolving programs with different representational and computational properties.
About gplearn
trevorstephens/gplearn
Genetic Programming in Python, with a scikit-learn inspired API
Evolves mathematical expressions through genetic algorithms to discover symbolic regression models, supporting regression, binary classification, and automated feature engineering. Integrates seamlessly with scikit-learn's pipeline and grid search utilities via standard fit/predict API. Uses population-based selection and genetic operations (crossover, mutation) to iteratively improve program fitness against target variables.
About pyshgp
erp12/pyshgp
Push Genetic Programming in Python.
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