When to Use sklearn-genetic-opt

This page helps you decide whether a genetic search is the right tool for your tuning problem and shows what a realistic setup looks like.

Prerequisites

Basic familiarity with scikit-learn's GridSearchCV or RandomizedSearchCV
sklearn-genetic-opt installed (pip install sklearn-genetic-opt)

Choosing a Search Method

Method	Best for	Weakness	Typical space size
`GridSearchCV`	Small, fully discrete grids	Candidate count multiplies with each dimension	≤ 3 parameters
`RandomizedSearchCV`	Continuous spaces, large grids	Treats every parameter independently — misses interactions	3–6 parameters
`GASearchCV`	Mixed or large spaces with parameter interactions	Adds overhead on trivially small spaces	5+ parameters

The key limitation of random search is independence: it samples each parameter as if the others do not exist. A genetic algorithm recombines complete configurations that performed well, so it naturally gravitates toward combinations that work together.

Signs That GA Will Help

Five or more hyperparameters. The search space grows exponentially.
Known or suspected parameter interactions. learning_rate × number of estimators, regularization strength × solver.
Mixed parameter types. Integers, floats, and categoricals in one search.
Expensive evaluations. GA caches every evaluated candidate.
You want to narrow the space iteratively. plot_search_space shows where the algorithm sampled.

Signs That GA Will Not Help

One or two continuous parameters.
Very fast evaluations with a large budget.
A fully discrete grid with few values.

Example: Gradient Boosting With Seven Parameters

python

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import StratifiedKFold, train_test_split
from sklearn.metrics import roc_auc_score

from sklearn_genetic import EvolutionConfig, GASearchCV, PopulationConfig, RuntimeConfig
from sklearn_genetic.space import Categorical, Continuous, Integer

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42
)

param_grid = {
    "learning_rate": Continuous(0.01, 0.3, distribution="log-uniform"),
    "max_iter": Integer(50, 300),
    "max_depth": Integer(2, 8),
    "min_samples_leaf": Integer(5, 50),
    "l2_regularization": Continuous(1e-6, 1.0, distribution="log-uniform"),
    "max_features": Continuous(0.3, 1.0),
    "max_leaf_nodes": Integer(15, 127),
}

search = GASearchCV(
    estimator=HistGradientBoostingClassifier(random_state=42, early_stopping=False),
    param_grid=param_grid,
    cv=StratifiedKFold(n_splits=3, shuffle=True, random_state=42),
    scoring="roc_auc",
    evolution_config=EvolutionConfig(population_size=20, generations=15),
    population_config=PopulationConfig(initializer="smart"),
    runtime_config=RuntimeConfig(n_jobs=-1, parallel_backend="auto", use_cache=True),
)

search.fit(X_train, y_train)
print("Best CV score:", round(search.best_score_, 4))
print("Best parameters:", search.best_params_)

When to Use sklearn-genetic-opt ​

Prerequisites ​

Choosing a Search Method ​

Signs That GA Will Help ​

Signs That GA Will Not Help ​

Example: Gradient Boosting With Seven Parameters ​

Next Steps ​