evo-hp - Evolutionary Hyperparameter Optimizer

A scikit-learn-compatible hyperparameter optimizer for tabular data, powered by DEAP. evo-hp tunes the hyperparameters of any scikit-learn estimator with a Genetic Algorithm over mixed search spaces (integers, floats, log-scale, categoricals, booleans and conditional parameters), complementing evo-gafs (feature selection) and evo-gpfe (feature engineering) upstream, and evo-ens (ensemble construction) downstream.

Part of the evo-suite family (import name: evo_hp). Documentation: https://evo-suite.readthedocs.io/

Why evo-hp?

Installation

pip install evo-hp            # core
pip install "evo-hp[viz]"     # + matplotlib for the plotting helpers

Quickstart

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from evo_hp import EvoHPClassifier, EvoHPConfig

X, y = load_breast_cancer(return_X_y=True, as_frame=True)

clf = EvoHPClassifier(
    estimator=RandomForestClassifier(random_state=42),
    param_space="rf_clf",
    config=EvoHPConfig(population_size=40, n_generations=30, verbose=False),
)
clf.fit(X, y)

print(clf.result_.summary())
print(clf.best_params_)

EvoHPRegressor follows the same API for regression targets, scoring with R² by default. Define a custom search space with Int/Float/Cat/ Bool/Cond instead of a predefined name for full control:

from sklearn.svm import SVC
from evo_hp import EvoHPClassifier, Float, Cat, Cond, Int

space = [
    Float("C", 0.001, 1000.0, log=True),
    Cat("kernel", ["rbf", "linear", "poly", "sigmoid"]),
    Float("gamma", 1e-5, 10.0, log=True),
    Cond("degree", Int("degree", 2, 5), "kernel", ["poly"], default=3),
]
clf = EvoHPClassifier(estimator=SVC(max_iter=10_000), param_space=space)

Since evo-hp does not transform X, it slots in naturally as the final step of a Pipeline — including right after evo-gafs's feature selector, mirroring the suite's intended pipeline position (evoimp -> gpfe -> gafs -> evohp -> evoens):

from sklearn.pipeline import Pipeline
from evo_gafs import GAFeatureSelector
from evo_hp import EvoHPClassifier

pipe = Pipeline([
    ("select", GAFeatureSelector(estimator=RandomForestClassifier(random_state=42))),
    ("tune", EvoHPClassifier(estimator=RandomForestClassifier(random_state=42), param_space="rf_clf")),
])
pipe.fit(X, y)

Documentation & examples

• Full documentation (user guide + API reference): https://evo-suite.readthedocs.io/
• Runnable examples: the repository's examples/evo-hp/ directory.

Citation

@software{evo_hp,
  author    = {Skrauba, Axel},
  title     = {evo-hp: Evolutionary Hyperparameter Optimizer for tabular data},
  year      = {2026},
  version   = {0.1.0},
  url       = {https://github.com/AxelSkrauba/evo-suite}
}

License

MIT