Algorithms
The sklearn_genetic.algorithms module provides the DEAP-based evolutionary algorithm implementations used internally by GASearchCV and GAFeatureSelectionCV.
These are low-level functions — you typically do not call them directly. They are exposed for advanced users who want to inspect or extend the evolutionary logic.
python
from sklearn_genetic.algorithms import eaMuPlusLambda, eaMuCommaLambda, eaSimpleeaMuPlusLambda
The (μ + λ) evolutionary strategy. The next generation is selected from the union of parents and offspring. This is the default algorithm used by GASearchCV.
- Pros: Elitist — the best individuals are never lost.
- Cons: Can converge prematurely if the initial population is too small.
eaMuCommaLambda
The (μ, λ) evolutionary strategy. The next generation is selected from offspring only. Parents do not survive directly.
- Pros: Better exploration — avoids stagnation around a local optimum.
- Cons: Can lose the best individual between generations (non-elitist).
eaSimple
A simple generational genetic algorithm. Each generation fully replaces the previous one.
- Pros: Simple and fast.
- Cons: No explicit elitism — the best solution can be lost.
See Also
- DEAP documentation — upstream algorithm documentation
- Advanced Optimizer Control — configure algorithm behavior via
EvolutionConfig