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.

from sklearn_genetic.algorithms import eaMuPlusLambda, eaMuCommaLambda, eaSimple, eaDifferentialEvolution

eaMuPlusLambda

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: Parents compete with offspring; with elitism=True, archived best candidates retain survivor slots.
• 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: Without elitism=True, offspring-only survival can lose the best individual between generations.

eaSimple

A simple generational genetic algorithm. Each generation fully replaces the previous one.

• Pros: Simple and fast.
• Cons: Without elitism=True, generational replacement can lose the best individual.

eaDifferentialEvolution

An optional differential-evolution strategy that generates one trial per parent and retains the better candidate. It requires a population of at least four. Both search classes accept algorithm="eaDifferentialEvolution". Numeric dimensions use their declared linear or log domain; categorical choices and feature masks stay within their valid domains. It is an alternative to benchmark for your task, not a generally faster or more accurate replacement for the default algorithm.

See Also

• DEAP documentation — upstream algorithm documentation
• Advanced Optimizer Control — configure algorithm behavior via EvolutionConfig

Diversity telemetry

history["genotype_diversity"] is the mean probability that two distinct population members have different values at a gene. Identical populations score zero; populations with distinct values at every gene score one. Balanced binary genes score approximately 0.5 even in large populations. unique_individual_ratio continues to describe the fraction of distinct complete candidates.

This corrects the older distinct-allele normalization that made large feature populations appear collapsed. Custom diversity thresholds may need recalibration. Local integer refinement now samples directly from the bounded neighborhood, excluding the parent's value when alternatives exist, with constant memory.

Elites and progress

With elitism=True, the mu/lambda algorithms reserve up to keep_top_k survivor slots for copies of the best archived candidates. With elitism=False, all survivors are selected from the algorithm's candidate pool. In both cases, fitness_best tracks the best evaluated candidate in the hall of fame, while fitness_min, fitness_max and fitness describe the current population.