evo-gafs 0.1.0

Genetic Algorithm Feature Selector — a scikit-learn-compatible wrapper feature selector for tabular data (evo-suite)

evo-gafs — Genetic Algorithm Feature Selector

A scikit-learn-compatible wrapper feature selector for tabular data, powered by DEAP. evo-gafs searches for the subset of features that maximises a cross-validated score of your model, and lets you explicitly trade raw performance for a smaller feature set — useful for edge deployment.

Part of the evo-suite family (import name: evo_gafs).

Why evo-gafs?

Capability	evo-gafs
Single-objective weighted fitness with a configurable alpha (performance ↔ compression)	✓
Multi-objective NSGA-II with an accessible Pareto front	✓
Repair operator guaranteeing a minimum number of features	✓
Evaluation cache to skip repeated genomes	✓
Native scikit-learn fit/transform/get_support, usable in a Pipeline	✓
Built-in multi-dataset BenchmarkRunner	✓

Installation

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

Quickstart

from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from evo_gafs import GAFeatureSelector, GAConfig

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

selector = GAFeatureSelector(
    estimator=DecisionTreeClassifier(random_state=42),
    config=GAConfig(population_size=30, n_generations=20, alpha=0.8, verbose=False),
)
selector.fit(X, y)

print(selector.summary())
X_reduced = selector.transform(X)
print("Selected:", selector.get_support(indices=True))

Multi-objective (Pareto front)

config = GAConfig(mode="multiobjective", population_size=40, n_generations=30, verbose=False)
selector = GAFeatureSelector(estimator=DecisionTreeClassifier(random_state=42), config=config)
selector.fit(X, y)

for point in selector.result_.pareto_front:
    print(point["n_features"], point["cv_score"])

In a scikit-learn pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("selector", GAFeatureSelector(estimator=DecisionTreeClassifier(), config=config)),
    ("clf", SVC()),
])
pipe.fit(X, y)

The alpha trade-off (single-objective)

fitness = alpha * cv_score + (1 - alpha) * compression
compression = 1 - n_selected / n_total

• alpha = 1.0 → pure wrapper (performance only)
• alpha ≈ 0.7 → balanced, good default for edge deployment

Citation

@software{evo_gafs,
  author    = {Skrauba, Axel},
  title     = {evo-gafs: Genetic Algorithm Feature Selector for tabular data},
  year      = {2026},
  version   = {0.1.0},
  url       = {https://github.com/AxelSkrauba/evo-suite}
}

License

MIT