evo-suite

Evolutionary computation for tabular data engineering.

evo-suite is a family of independent, scikit-learn-compatible packages that apply evolutionary computation to the data-preprocessing stage of a machine-learning pipeline. They share one repository, CI and documentation, but are published to PyPI independently.

Distribution

Import

Technique

Role

Status

evo-gafs

evo_gafs

Genetic Algorithm

Feature selection

Available

evo-gpfe

evo_gpfe

Genetic Programming

Feature engineering

Planned

This documentation currently covers evo-gafs, a genetic-algorithm wrapper feature selector for tabular data.

Highlights

  • Explicit accuracy ↔ compression trade-off via a single alpha parameter — ideal for edge/embedded deployment.

  • Multi-objective NSGA-II mode exposing the full Pareto front.

  • Native scikit-learn estimator: fit / transform / get_support, usable in a Pipeline and tunable with GridSearchCV.

  • Repair operator guaranteeing feasible subsets, evaluation cache, and a built-in multi-dataset benchmark runner.